1  Creating R packages

Packages provide a mechanism for loading optional code, data and documentation as needed. The R distribution itself includes about 30 packages.

In the following, we assume that you know the library() command, including its lib.loc argument, and we also assume basic knowledge of the R CMD INSTALL utility. Otherwise, please look at R’s help pages on

?library
?INSTALL

before reading on.

For packages which contain code to be compiled, a computing environment including a number of tools is assumed; the “R Installation and Administration” manual describes what is needed for each OS.

Once a source package is created, it must be installed by the command R CMD INSTALL. See Add-on packages in R Installation and Administration.

Other types of extensions are supported (but rare): See Package types.

Some notes on terminology complete this introduction. These will help with the reading of this manual, and also in describing concepts accurately when asking for help.

A package is a directory of files which extend R, a source package (the master files of a package), or a tarball containing the files of a source package, or an installed package, the result of running R CMD INSTALL on a source package. On some platforms (notably macOS and x86_64 Windows) there are also binary packages, a zip file or tarball containing the files of an installed package which can be unpacked rather than installing from sources.

A package is not¹ a library. The latter is used in two senses in R documentation.

¹ although this is a persistent mis-usage. It seems to stem from S, whose analogues of R’s packages were officially known as library sections and later as chapters, but almost always referred to as libraries.

• A directory into which packages are installed, e.g. /usr/lib/R/library: in that sense it is sometimes referred to as a library directory or library tree (since the library is a directory which contains packages as directories, which themselves contain directories).
• That used by the operating system, as a shared, dynamic or static library or (especially on Windows) a DLL, where the second L stands for ‘library’. Installed packages may contain compiled code in what is known on Unix-alikes as a shared object and on Windows as a DLL. The concept of a shared library (dynamic library on macOS) as a collection of compiled code to which a package might link is also used, especially for R itself on some platforms. On most platforms these concepts are interchangeable (shared objects and DLLs can both be loaded into the R process and be linked against), but macOS distinguishes between shared objects (extension .so) and dynamic libraries (extension .dylib).

There are a number of well-defined operations on source packages.

• The most common is installation which takes a source package and installs it in a library using R CMD INSTALL or install.packages.
• Source packages can be built. This involves taking a source directory and creating a tarball ready for distribution, including cleaning it up and creating PDF/HTML documentation from any vignettes it may contain. Source packages (and most often tarballs) can be checked, when a test installation is done and tested (including running its examples); also, the contents of the package are tested in various ways for consistency and portability.
• Compilation is not a correct term for a package. Installing a source package which contains C, C++ or Fortran code will involve compiling that code. There is also the possibility of ‘byte’ compiling the R code in a package (using the facilities of package compiler): nowadays this is enabled by default for all packages. So compiling a package may come to mean byte-compiling its R code.
• It used to be unambiguous to talk about loading an installed package using library(), but since the advent of package namespaces this has been less clear: people now often talk about loading the package’s namespace and then attaching the package so it becomes visible on the search path. Function library performs both steps, but a package’s namespace can be loaded without the package being attached (for example by calls like splines::ns).

The concept of lazy loading of code or data is mentioned at several points. This is part of the installation, always selected for R code but optional for data. When used the R objects of the package are created at installation time and stored in a database in the R directory of the installed package, being loaded into the session at first use. This makes the R session start up faster and use less (virtual) memory. (For technical details, see Lazy loading in R Internals.)

CRAN is a network of WWW sites holding the R distributions and contributed code, especially R packages. Users of R are encouraged to join in the collaborative project and to submit their own packages to CRAN: current instructions are linked from https://CRAN.R-project.org/banner.shtml#submitting.

1.1 Package structure

The sources of an R package consist of a subdirectory containing the files DESCRIPTION and NAMESPACE, and the subdirectories R, data, demo, exec, inst, man, po, src, tests, tools and vignettes (some of which can be missing, but which should not be empty). The package subdirectory may also contain files INDEX, configure, cleanup, LICENSE, LICENCE and NEWS. Other files such as INSTALL (for non-standard installation instructions), README/README.md², or ChangeLog will be ignored by R, but may be useful to end users. The utility R CMD build may add files in a build directory (but this should not be used for other purposes).

² This seems to be commonly used for a file in ‘markdown’ format. Be aware that most users of R will not know that, nor know how to view such a file: platforms such as macOS and Windows do not have a default viewer set in their file associations. The CRAN package web pages render such files in HTML: the converter used expects the file to be encoded in UTF-8.

³ currently, top-level files .Rbuildignore and .Rinstignore, and vignettes/.install_extras.

Except where specifically mentioned,³ packages should not contain Unix-style ‘hidden’ files/directories (that is, those whose name starts with a dot).

The DESCRIPTION and INDEX files are described in the subsections below. The NAMESPACE file is described in the section on Package namespaces.

The optional files configure and cleanup are (Bourne) shell scripts which are, respectively, executed before and (if option --clean was given) after installation on Unix-alikes, see Configure and cleanup. The analogues on Windows are configure.win and cleanup.win. Since R 4.2.0 on Windows, configure.ucrt and cleanup.ucrt are supported and take precedence over configure.win and cleanup.win. They can hence be used to provide content specific to UCRT or Rtools42 and newer, if needed, but the support for .ucrt files may be removed in future when building packages from source on the older versions of R will no longer be needed, and hence the files may be renamed back to .win.

For the conventions for files NEWS and ChangeLog in the GNU project see https://www.gnu.org/prep/standards/standards.html#Documentation.

The package subdirectory should be given the same name as the package. Because some file systems (e.g., those on Windows and by default on macOS) are not case-sensitive, to maintain portability it is strongly recommended that case distinctions not be used to distinguish different packages. For example, if you have a package named foo, do not also create a package named Foo.

To ensure that file names are valid across file systems and supported operating systems, the ASCII control characters as well as the characters ", *, :, /, <, >, ?, \, and | are not allowed in file names. In addition, files with names con, prn, aux, clock$, nul, com1 to com9, and lpt1 to lpt9 after conversion to lower case and stripping possible “extensions” (e.g., lpt5.foo.bar), are disallowed. Also, file names in the same directory must not differ only by case (see the previous paragraph). In addition, the basenames of .Rd files may be used in URLs and so must be ASCII and not contain %. For maximal portability filenames should only contain only ASCII characters not excluded already (that is A-Za-z0-9._!#$%&+,;=@^(){}'[] — we exclude space as many utilities do not accept spaces in file paths): non-English alphabetic characters cannot be guaranteed to be supported in all locales. It would be good practice to avoid the shell metacharacters (){}'[]$~: ~ is also used as part of ‘8.3’ filenames on Windows. In addition, some applications on Windows can only work with path names of certain length, following an earlier limit in the Windows operating system. Packages are normally distributed as tarballs, and these have a limit on path lengths. So, to be friendly to users who themselves may want to use a relatively long path where they extract the package, and for maximal portability, 100 bytes.

A source package if possible should not contain binary executable files: they are not portable, and a security risk if they are of the appropriate architecture. R CMD check will warn about them⁴ unless they are listed (one filepath per line) in a file BinaryFiles at the top level of the package. Note that CRAN will not accept submissions containing binary files even if they are listed.

⁴ false positives are possible, but only a handful have been seen so far.

The R function package.skeleton can help to create the structure for a new package: see its help page for details.

1.1.1 The DESCRIPTION file

The DESCRIPTION file contains basic information about the package in the following format:

Package: pkgname Version: 0.5-1 Date: 2015-01-01 Title: My First Collection of Functions Authors@R: c(person("Joe", "Developer", role = c("aut", "cre"), email = "Joe.Developer@some.domain.net", comment = c(ORCID = "nnnn-nnnn-nnnn-nnnn")), person("Pat", "Developer", role = "aut"), person("A.", "User", role = "ctb", email = "A.User@whereever.net")) Author: Joe Developer [aut, cre], Pat Developer [aut], A. User [ctb] Maintainer: Joe Developer <Joe.Developer@some.domain.net> Depends: R (>= 3.1.0), nlme Suggests: MASS Description: A (one paragraph) description of what the package does and why it may be useful. License: GPL (>= 2) URL: https://www.r-project.org, http://www.another.url BugReports: https://pkgname.bugtracker.url

The format is that of a version of a ‘Debian Control File’ (see the help for read.dcf and https://www.debian.org/doc/debian-policy/ch-controlfields.html: R does not require encoding in UTF-8 and does not support comments starting with #). Fields start with an ASCII name immediately followed by a colon: the value starts after the colon and a space. Continuation lines (for example, for descriptions longer than one line) start with a space or tab. Field names are case-sensitive: all those used by R are capitalized.

For maximal portability, the DESCRIPTION file should be written entirely in ASCII — if this is not possible it must contain an Encoding field (see below).

Several optional fields take logical values: these can be specified as yes, true, no or false: capitalized values are also accepted.

The Package, Version, License, Description, Title, Author, and Maintainer fields are mandatory, all other fields are optional. Fields Author and Maintainer can be auto-generated from Authors@R, and may be omitted if the latter is provided: however if they are not ASCII we recommend that they are provided.

The mandatory Package field gives the name of the package. This should contain only (ASCII) letters, numbers and dot, have at least two characters and start with a letter and not end in a dot. If it needs explaining, this should be done in the Description field (and not the Title field).

The mandatory Version field gives the version of the package. This is a sequence of at least two (and usually three) non-negative integers separated by single . or - characters. The canonical form is as shown in the example, and a version such as 0.01 or 0.01.0 will be handled as if it were 0.1-0. It is not a decimal number, so for example 0.9 < 0.75 since 9 < 75.

The mandatory License field is discussed in the next subsection.

The mandatory Title field should give a short description of the package. Some package listings may truncate the title to 65 characters. It should use title case (that is, use capitals for the principal words: tools::toTitleCase can help you with this), not use any markup, not have any continuation lines, and not end in a period (unless part of …). Do not repeat the package name: it is often used prefixed by the name. Refer to other packages and external software in single quotes, and to book titles (and similar) in double quotes.

The mandatory Description field should give a comprehensive description of what the package does. One can use several (complete) sentences, but only one paragraph. It should be intelligible to all the intended readership (e.g. for a CRAN package to all CRAN users). It is good practice not to start with the package name, ‘This package’ or similar. As with the Title field, double quotes should be used for quotations (including titles of books and articles), and single quotes for non-English usage, including names of other packages and external software. This field should also be used for explaining the package name if necessary. URLs should be enclosed in angle brackets, e.g. <https://www.r-project.org>: see also Specifying URLs.

The mandatory Author field describes who wrote the package. It is a plain text field intended for human readers, but not for automatic processing (such as extracting the email addresses of all listed contributors: for that use Authors@R). Note that all significant contributors must be included: if you wrote an R wrapper for the work of others included in the src directory, you are not the sole (and maybe not even the main) author.

The mandatory Maintainer field should give a single name followed by a valid (RFC 2822) email address in angle brackets. It should not end in a period or comma. This field is what is reported by the maintainer function and used by bug.report. For a CRAN package it should be a person, not a mailing list and not a corporate entity: do ensure that it is valid and will remain valid for the lifetime of the package.

Note that the display name (the part before the address in angle brackets) should be enclosed in double quotes if it contains non-alphanumeric characters such as comma or period. (The current standard, RFC 5322, allows periods but RFC 2822 did not.)

Both Author and Maintainer fields can be omitted if a suitable Authors@R field is given. This field can be used to provide a refined and machine-readable description of the package “authors” (in particular specifying their precise roles), via suitable R code. It should create an object of class "person", by either a call to person or a series of calls (one per “author”) concatenated by c(): see the example DESCRIPTION file above. The roles can include "aut" (author) for full authors, "cre" (creator) for the package maintainer, and "ctb" (contributor) for other contributors, "cph" (copyright holder, which should be the legal name for an institution or corporate body), among others. See ?person for more information. Note that no role is assumed by default. Auto-generated package citation information takes advantage of this specification. The Author and Maintainer fields are auto-generated from it if needed when building⁵ or installing. Note that for CRAN submissions, providing Authors@R is required, and providing ORCID identifiers (see https://orcid.org/) where possible is strongly encouraged.

⁵ at least if this is done in a locale which matches the package encoding.

An optional Copyright field can be used where the copyright holder(s) are not the authors. If necessary, this can refer to an installed file: the convention is to use file inst/COPYRIGHTS.

The optional Date field gives the release date of the current version of the package. It is strongly recommended⁶ to use the yyyy-mm-dd format conforming to the ISO 8601 standard.

⁶ and required by CRAN, so checked by R CMD check --as-cran.

The Depends, Imports, Suggests, Enhances, LinkingTo and Additional_repositories fields are discussed in a later subsection.

Dependencies external to the R system should be listed in the SystemRequirements field, possibly amplified in a separate README file. This includes specifying a non-default C++ standard and the need for GNU make.

The URL field may give a list of URLs separated by commas or whitespace, for example the homepage of the author or a page where additional material describing the software can be found. These URLs are converted to active hyperlinks in CRAN package listings. See Specifying URLs.

The BugReports field may contain a single URL to which bug reports about the package should be submitted. This URL will be used by bug.report instead of sending an email to the maintainer. A browser is opened for a http:// or https:// URL. To specify another email address for bug reports, use Contact instead: however bug.report will try to extract an email address (preferably from a mailto: URL or enclosed in angle brackets) from BugReports.

Base and recommended packages (i.e., packages contained in the R source distribution or available from CRAN and recommended to be included in every binary distribution of R) have a Priority field with value base or recommended, respectively. These priorities must not be used by other packages.

A Collate field can be used for controlling the collation order for the R code files in a package when these are processed for package installation. The default is to collate according to the C locale. If present, the collate specification must list all R code files in the package (taking possible OS-specific subdirectories into account, see Package subdirectories) as a whitespace separated list of file paths relative to the R subdirectory. Paths containing white space or quotes need to be quoted. An OS-specific collation field (Collate.unix or Collate.windows) will be used in preference to Collate.

The LazyData logical field controls whether the R datasets use lazy-loading. A LazyLoad field was used in versions prior to 2.14.0, but now is ignored.

The KeepSource logical field controls if the package code is sourced using keep.source = TRUE or FALSE: it might be needed exceptionally for a package designed to always be used with keep.source = TRUE.

The ByteCompile logical field controls if the package R code is to be byte-compiled on installation: the default is to byte-compile. This can be overridden by installing with flag --no-byte-compile.

The UseLTO logical field is used to indicate if source code in the package⁷ is to be compiled with Link-Time Optimization (see Using Link-time Optimization) if R was installed with --enable-lto (default true) or --enable-lto=R (default false) (or on Windows⁸ if LTO_OPT is set in MkRules). This can be overridden by the flags --use-LTO and --no-use-LTO. LTO is said to give most size and performance improvements for large and complex (heavily templated) C++ projects.

⁷ without a src/Makefile* file.

⁸ LTO is not currently supported by the toolchain used on aarch64.

The StagedInstall logical field controls if package installation is ‘staged’, that is done to a temporary location and moved to the final location when successfully completed. This field was introduced in R 3.6.0 and it true by default: it is considered to be a temporary measure which may be withdrawn in future.

The ZipData logical field has been ignored since R 2.13.0.

The Biarch logical field is used on Windows to select the INSTALL option --force-biarch for this package. Not currently relevant.

The BuildVignettes logical field can be set to a false value to stop R CMD build from attempting to build the vignettes, as well as preventing⁹ R CMD check from testing this. This should only be used exceptionally, for example if the PDFs include large figures which are not part of the package sources (and hence only in packages which do not have an Open Source license).

⁹ But it is checked for Open Source packages by R CMD check --as-cran.

The VignetteBuilder field names (in a comma-separated list) packages that provide an engine for building vignettes. These may include the current package, or ones listed in Depends, Suggests or Imports. The utils package is always implicitly appended. See Non-Sweave vignettes for details. Note that if, for example, a vignette has engine knitr::rmarkdown, then knitr provides the engine but both knitr and rmarkdown are needed for using it, so both these packages need to be in the VignetteBuilder field and at least suggested (as rmarkdown is only suggested by knitr, and hence not available automatically along with it). Many packages using knitr also need the package formatR which it suggests and so the user package needs to do so too and include this in VignetteBuilder.

If the DESCRIPTION file is not entirely in ASCII it should contain an Encoding field specifying an encoding. This is used as the encoding of the DESCRIPTION file itself and of the R and NAMESPACE files, and as the default encoding of .Rd files. The examples are assumed to be in this encoding when running R CMD check, and it is used for the encoding of the CITATION file. Only encoding names latin1 and and UTF-8 are known to be portable. (Do not specify an encoding unless one is actually needed: doing so makes the package less portable. If a package has a specified encoding, you should run R CMD build etc in a locale using that encoding.)

The NeedsCompilation field should be set to "yes" if the package contains native code which needs to be compiled, otherwise "no" (when the package could be installed from source on any platform without additional tools). This is used by install.packages(type = "both") in R >= 2.15.2 on platforms where binary packages are the norm: it is normally set by R CMD build or the repository assuming compilation is required if and only if the package has a src directory.

The OS_type field specifies the OS(es) for which the package is intended. If present, it should be one of unix or windows, and indicates that the package can only be installed on a platform with .Platform$OS.type having that value.

The Type field specifies the type of the package: see Package types.

One can add subject classifications for the content of the package using the fields Classification/ACM or Classification/ACM-2012 (using the Computing Classification System of the Association for Computing Machinery, https://www.acm.org/publications/class-2012; the former refers to the 1998 version), Classification/JEL (the Journal of Economic Literature Classification System, https://www.aeaweb.org/econlit/jelCodes.php, or Classification/MSC or Classification/MSC-2010 (the Mathematics Subject Classification of the American Mathematical Society, https://mathscinet.ams.org/msc/msc2010.html; the former refers to the 2000 version). The subject classifications should be comma-separated lists of the respective classification codes, e.g., Classification/ACM: G.4, H.2.8, I.5.1.

A Language field can be used to indicate if the package documentation is not in English: this should be a comma-separated list of standard (not private use or grandfathered) IETF language tags as currently defined by RFC 5646 (https://www.rfc-editor.org/rfc/rfc5646, see also https://en.wikipedia.org/wiki/IETF_language_tag), i.e., use language subtags which in essence are 2-letter ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) or 3-letter ISO 639-3 (https://en.wikipedia.org/wiki/ISO_639-3) language codes.

An RdMacros field can be used to hold a comma-separated list of packages from which the current package will import Rd macro definitions. These package should also be listed in Imports (or Depends). The macros in these packages will be imported after the system macros, in the order listed in the RdMacros field, before any macro definitions in the current package are loaded. Macro definitions in individual .Rd files in the man directory are loaded last, and are local to later parts of that file. In case of duplicates, the last loaded definition will be used.¹⁰ Both R CMD Rd2pdf and R CMD Rdconv have an optional flag --RdMacros=pkglist. The option is also a comma-separated list of package names, and has priority over the value given in DESCRIPTION. Packages using Rd macros should depend on R 3.2.0 or later.

¹⁰ Duplicate definitions may trigger a warning: see User-defined macros.

Note: There should be no Built or Packaged fields, as these are added by the package management tools.

There is no restriction on the use of other fields not mentioned here (but using other capitalizations of these field names would cause confusion). Fields Note, Contact (for contacting the authors/developers¹¹) and MailingList are in common use. Some repositories (including CRAN and R-forge) add their own fields.

¹¹ bug.report will try to extract an email address from a Contact field if there is no BugReports field.

1.1.2 Licensing

Licensing for a package which might be distributed is an important but potentially complex subject.

It is very important that you include license information! Otherwise, it may not even be legally correct for others to distribute copies of the package, let alone use it.

The package management tools use the concept of ‘free or open source software’ (FOSS, e.g., https://en.wikipedia.org/wiki/FOSS) licenses: the idea being that some users of R and its packages want to restrict themselves to such software. Others need to ensure that there are no restrictions stopping them using a package, e.g. forbidding commercial or military use. It is a central tenet of FOSS software that there are no restrictions on users nor usage.

Do not use the License field for information on copyright holders: if needed, use a Copyright field.

The mandatory License field in the DESCRIPTION file should specify the license of the package in a standardized form. Alternatives are indicated via vertical bars. Individual specifications must be one of

• One of the “standard” short specifications

  GPL-2 GPL-3 LGPL-2 LGPL-2.1 LGPL-3 AGPL-3 Artistic-2.0
  BSD_2_clause BSD_3_clause MIT

  as made available via https://www.R-project.org/Licenses/ and contained in subdirectory share/licenses of the R source or home directory.

• The names or abbreviations of other licenses contained in the license data base in file share/licenses/license.db in the R source or home directory, possibly (for versioned licenses) followed by a version restriction of the form (op v) with op one of the comparison operators <, <=, >, >=, ==, or != and v a numeric version specification (strings of non-negative integers separated by .), possibly combined via , (see below for an example). For versioned licenses, one can also specify the name followed by the version, or combine an existing abbreviation and the version with a -.

  Abbreviations GPL and LGPL are ambiguous and usually¹² taken to mean any version of the license: but it is better not to use them.

• One of the strings file LICENSE or file LICENCE referring to a file named LICENSE or LICENCE in the package (source and installation) top-level directory.

• The string Unlimited, meaning that there are no restrictions on distribution or use other than those imposed by relevant laws (including copyright laws).

¹² CRAN expands them to e.g. GPL-2 | GPL-3.

Multiple licences can be specified separated by | (surrounded by spaces) in which case the user can choose any of the alternatives.

If a package license restricts a base license (where permitted, e.g., using GPL-3 or AGPL-3 with an attribution clause), the additional terms should be placed in file LICENSE (or LICENCE), and the string + file LICENSE (or + file LICENCE, respectively) should be appended to the corresponding individual license specification (preferably with the + surrounded by spaces). Note that several commonly used licenses do not permit restrictions: this includes GPL-2 and hence any specification which includes it.

Examples of standardized specifications include

License: GPL-2
License: LGPL (>= 2.0, < 3) | Mozilla Public License
License: GPL-2 | file LICENCE
License: GPL (>= 2) | BSD_3_clause + file LICENSE
License: Artistic-2.0 | AGPL-3 + file LICENSE

Please note in particular that “Public domain” is not a valid license, since it is not recognized in some jurisdictions.

Please ensure that the license you choose also covers any dependencies (including system dependencies) of your package: it is particularly important that any restrictions on the use of such dependencies are evident to people reading your DESCRIPTION file.

Fields License_is_FOSS and License_restricts_use may be added by repositories where information cannot be computed from the name of the license. License_is_FOSS: yes is used for licenses which are known to be FOSS, and License_restricts_use can have values yes or no if the LICENSE file is known to restrict users or usage, or known not to. These are used by, e.g., the available.packages filters.

The optional file LICENSE/LICENCE contains a copy of the license of the package. To avoid any confusion only include such a file if it is referred to in the License field of the DESCRIPTION file.

Whereas you should feel free to include a license file in your source distribution, please do not arrange to install yet another copy of the GNU COPYING or COPYING.LIB files but refer to the copies on https://www.R-project.org/Licenses/ and included in the R distribution (in directory share/licenses). Since files named LICENSE or LICENCE will be installed, do not use these names for standard license files. To include comments about the licensing rather than the body of a license, use a file named something like LICENSE.note.

A few “standard” licenses are rather license templates which need additional information to be completed via + file LICENSE (with the + surrounded by spaces). Where the additional information is COPYRIGHT HOLDER, this must give the actual legal entities (not something vague like ‘Name-of-package authors’): if more than one they should be listed in decreasing order of contribution.

1.1.3 Package Dependencies

The Depends field gives a comma-separated list of package names which this package depends on. Those packages will be attached before the current package when library or require is called. Each package name may be optionally followed by a comment in parentheses specifying a version requirement. The comment should contain a comparison operator, whitespace and a valid version number, e.g. MASS (>= 3.1-20).

The Depends field can also specify a dependence on a certain version of R — e.g., if the package works only with R version 4.0.0 or later, include R (>= 4.0) in the Depends field. (As here, trailing zeroes can be dropped and it is recommended that they are.) You can also require a certain SVN revision for R-devel or R-patched, e.g. R (>= 2.14.0), R (>= r56550) requires a version later than R-devel of late July 2011 (including released versions of 2.14.0).

It makes no sense to declare a dependence on R without a version specification, nor on the package base: this is an R package and package base is always available.

A package or R can appear more than once in the Depends field, for example to give upper and lower bounds on acceptable versions.

It is inadvisable to use a dependence on R with patch level (the third digit) other than zero. Doing so with packages which others depend on will cause the other packages to become unusable under earlier versions in the series, and e.g. versions 4.x.1 are widely used throughout the Northern Hemisphere academic year.

Both library and the R package checking facilities use this field: hence it is an error to use improper syntax or misuse the Depends field for comments on other software that might be needed. The R INSTALL facilities check if the version of R used is recent enough for the package being installed, and the list of packages which is specified will be attached (after checking version requirements) before the current package.

The Imports field lists packages whose namespaces are imported from (as specified in the NAMESPACE file) but which do not need to be attached. Namespaces accessed by the :: and ::: operators must be listed here, or in Suggests or Enhances (see below). Ideally this field will include all the standard packages that are used, and it is important to include S4-using packages (as their class definitions can change and the DESCRIPTION file is used to decide which packages to re-install when this happens). Packages declared in the Depends field should not also be in the Imports field. Version requirements can be specified and are checked when the namespace is loaded.

The Suggests field uses the same syntax as Depends and lists packages that are not necessarily needed. This includes packages used only in examples, tests or vignettes (see Writing package vignettes), and packages loaded in the body of functions. E.g., suppose an example¹³ from package foo uses a dataset from package bar. Then it is not necessary to have bar use foo unless one wants to execute all the examples/tests/vignettes: it is useful to have bar, but not necessary. Version requirements can be specified but should be checked by the code which uses the package.

¹³ even one wrapped in \donttest.

Finally, the Enhances field lists packages “enhanced” by the package at hand, e.g., by providing methods for classes from these packages, or ways to handle objects from these packages (so several packages have Enhances: chron because they can handle datetime objects from chron even though they prefer R’s native datetime functions). Version requirements can be specified, but are currently not used. Such packages cannot be required to check the package: any tests which use them must be conditional on the presence of the package. (If your tests use e.g. a dataset from another package it should be in Suggests and not Enhances.)

The general rules are

• A package should be listed in only one of these fields.
• Packages whose namespace only is needed to load the package using library(pkgname) should be listed in the Imports field and not in the Depends field. Packages listed in import or importFrom directives in the NAMESPACE file should almost always be in Imports and not Depends.
• Packages that need to be attached to successfully load the package using library(pkgname) must be listed in the Depends field.
• All packages that are needed¹⁴ to successfully run R CMD check on the package must be listed in one of Depends or Suggests or Imports. Packages used to run examples or tests conditionally (e.g. via if(require(pkgname))) should be listed in Suggests or Enhances. (This allows checkers to ensure that all the packages needed for a complete check are installed.)
• Packages needed to use datasets from the package should be in Imports: this includes those needed to define S4 classes used.

¹⁴ This includes all packages directly called by library and require calls, as well as data obtained via data(theirdata, package = "somepkg") calls: R CMD check will warn about all of these. But there are subtler uses which it may not detect: e.g. if package A uses package B and makes use of functionality in package B which uses package C which package B suggests or enhances, then package C needs to be in the Suggests list for package A. Nor will undeclared uses in included files be reported, nor unconditional uses of packages listed under Enhances. R CMD check --as-cran will detect more of the subtler uses.

In particular, packages providing “only” data for examples or vignettes should be listed in Suggests rather than Depends in order to make lean installations possible.

Version dependencies in the Depends and Imports fields are used by library when it loads the package, and install.packages checks versions for the Depends, Imports and (for dependencies = TRUE) Suggests fields.

It is important that the information in these fields is complete and accurate: it is for example used to compute which packages depend on an updated package and which packages can safely be installed in parallel.

This scheme was developed before all packages had namespaces (R 2.14.0 in October 2011), and good practice changed once that was in place.

Field Depends should nowadays be used rarely, only for packages which are intended to be put on the search path to make their facilities available to the end user (and not to the package itself): for example it makes sense that a user of package latticeExtra would want the functions of package lattice made available.

Almost always packages mentioned in Depends should also be imported from in the NAMESPACE file: this ensures that any needed parts of those packages are available when some other package imports the current package.

The Imports field should not contain packages which are not imported from (via the NAMESPACE file or :: or ::: operators), as all the packages listed in that field need to be installed for the current package to be installed. (This is checked by R CMD check.)

R code in the package should call library or require only exceptionally. Such calls are never needed for packages listed in Depends as they will already be on the search path. It used to be common practice to use require calls for packages listed in Suggests in functions which used their functionality, but nowadays it is better to access such functionality via :: calls.

A package that wishes to make use of header files in other packages to compile its C/C++ code needs to declare them as a comma-separated list in the field LinkingTo in the DESCRIPTION file. For example

LinkingTo: link1, link2

The LinkingTo field can have a version requirement which is checked at installation.

Specifying a package in LinkingTo suffices if these are C/C++ headers containing source code or static linking is done at installation: the packages do not need to be (and usually should not be) listed in the Depends or Imports fields. This includes CRAN package BH and almost all users of RcppArmadillo and RcppEigen. Note that LinkingTo applies only to installation: if a packages wishes to use headers to compile code in tests or vignettes the package providing them needs to be listed in Suggests or perhaps Depends.

For another use of LinkingTo see Linking to native routines in other packages.

The Additional_repositories field is a comma-separated list of repository URLs where the packages named in the other fields may be found. It is currently used by R CMD check to check that the packages can be found, at least as source packages (which can be installed on any platform).

1.1.4 Suggested packages

Note that someone wanting to run the examples/tests/vignettes may not have a suggested package available (and it may not even be possible to install it for that platform). The recommendation used to be to make their use conditional via if(require("pkgname")): this is OK if that conditioning is done in examples/tests/vignettes, although using if(requireNamespace("pkgname")) is preferred, if possible.

However, using require for conditioning in package code is not good practice as it alters the search path for the rest of the session and relies on functions in that package not being masked by other require or library calls. It is better practice to use code like

   if (requireNamespace("rgl", quietly = TRUE)) {
      rgl::plot3d(...)
   } else {
      ## do something else not involving rgl.
   }

Note the use of rgl:: as that object would not necessarily be visible (and if it is, it need not be the one from that namespace: plot3d occurs in several other packages). If the intention is to give an error if the suggested package is not available, simply use e.g. rgl::plot3d.

If the conditional code produces print output, function withAutoprint can be useful.

Note that the recommendation to use suggested packages conditionally in tests does also apply to packages used to manage test suites: a notorious example was testthat which in version 1.0.0 contained illegal C++ code and hence could not be installed on standards-compliant platforms.

Some people have assumed that a ‘recommended’ package in Suggests can safely be used unconditionally, but this is not so. (R can be installed without recommended packages, and which packages are ‘recommended’ may change.)

As noted above, packages in Enhances must be used conditionally and hence objects within them should always be accessed via ::.

On most systems, R CMD check can be run with only those packages declared in Depends and Imports by setting environment variable _R_CHECK_DEPENDS_ONLY_=true, whereas setting _R_CHECK_SUGGESTS_ONLY_=true also allows suggested packages, but not those in Enhances nor those not mentioned in the DESCRIPTION file. It is recommended that a package is checked with each of these set, as well as with neither.

WARNING: Be extremely careful if you do things which would be run at installation time depending on whether suggested packages are available or not—this includes top-level code in R code files, .onLoad functions and the definitions of S4 classes and methods. The problem is that once a namespace of a suggested package is loaded, references to it may be captured in the installed package (most commonly in S4 methods), but the suggested package may not be available when the installed package is used (which especially for binary packages might be on a different machine). Even worse, the problems might not be confined to your package, for the namespaces of your suggested packages will also be loaded whenever any package which imports yours is installed and so may be captured there.

1.1.5 The INDEX file

The optional file INDEX contains a line for each sufficiently interesting object in the package, giving its name and a description (functions such as print methods not usually called explicitly might not be included). Normally this file is missing and the corresponding information is automatically generated from the documentation sources (using tools::Rdindex()) when installing from source.

The file is part of the information given by library(help = pkgname).

Rather than editing this file, it is preferable to put customized information about the package into an overview help page (see Documenting packages) and/or a vignette (see Writing package vignettes).

1.1.6 Package subdirectories

The R subdirectory contains R code files, only. The code files to be installed must start with an ASCII (lower or upper case) letter or digit and have one of the extensions¹⁵ .R, .S, .q, .r, or .s. We recommend using .R, as this extension seems to be not used by any other software. It should be possible to read in the files using source(), so R objects must be created by assignments. Note that there need be no connection between the name of the file and the R objects created by it. Ideally, the R code files should only directly assign R objects and definitely should not call functions with side effects such as require and options. If computations are required to create objects these can use code ‘earlier’ in the package (see the Collate field) plus functions in the Depends packages provided that the objects created do not depend on those packages except via namespace imports.

¹⁵ Extensions .S and .s arise from code originally written for S(-PLUS), but are commonly used for assembler code. Extension .q was used for S, which at one time was tentatively called QPE.

Extreme care is needed if top-level computations are made to depend on availability or not of other packages. In particular this applies to setMethods and setClass calls. Nor should they depend on the availability of external resources such as downloads.

Two exceptions are allowed: if the R subdirectory contains a file sysdata.rda (a saved image of one or more R objects: please use suitable compression as suggested by tools::resaveRdaFiles, and see also the SysDataCompression DESCRIPTION field.) this will be lazy-loaded into the namespace environment – this is intended for system datasets that are not intended to be user-accessible via data. Also, files ending in .in will be allowed in the R directory to allow a configure script to generate suitable files.

Only ASCII characters (and the control characters tab, form feed, LF and CR) should be used in code files. Other characters are accepted in comments¹⁶, but then the comments may not be readable in e.g. a UTF-8 locale. Non-ASCII characters in object names will normally¹⁷ fail when the package is installed. Any byte will be allowed in a quoted character string but \uxxxx escapes should be used for non-ASCII characters. However, non-ASCII character strings may not be usable in some locales and may display incorrectly in others.

¹⁶ but they should be in the encoding declared in the DESCRIPTION file.

¹⁷ This is true for OSes which implement the C locale: Windows’ idea of the C locale uses the WinAnsi charset.

Various R functions in a package can be used to initialize and clean up. See Load hooks.

The man subdirectory should contain (only) documentation files for the objects in the package in R documentation (Rd) format. The documentation filenames must start with an ASCII (lower or upper case) letter or digit and have the extension .Rd (the default) or .rd. Further, the names must be valid in file:// URLs, which means¹⁸ they must be entirely ASCII and not contain %. See Writing R documentation files, for more information. Note that all user-level objects in a package should be documented; if a package pkg contains user-level objects which are for “internal” use only, it should provide a file pkg-internal.Rd which documents all such objects, and clearly states that these are not meant to be called by the user. See e.g. the sources for package grid in the R distribution. Note that packages which use internal objects extensively should not export those objects from their namespace, when they do not need to be documented (see Package namespaces).

¹⁸ More precisely, they can contain the English alphanumeric characters and the symbols $ - _ . + ! ' ( ) , ;  = &.

Having a man directory containing no documentation files may give an installation error.

The man subdirectory may contain a subdirectory named macros; this will contain source for user-defined Rd macros. (See User-defined macros.) These use the Rd format, but may not contain anything but macro definitions, comments and whitespace.

The R and man subdirectories may contain OS-specific subdirectories named unix or windows.

The sources and headers for the compiled code are in src, plus optionally a file Makevars or Makefile (or for use on Windows, with extension .win or .ucrt). When a package is installed using R CMD INSTALL, make is used to control compilation and linking into a shared object for loading into R. There are default make variables and rules for this (determined when R is configured and recorded in R_HOME/etcR_ARCH/Makeconf), providing support for C, C++, fixed- or free-form Fortran, Objective C and Objective C++¹⁹ with associated extensions .c, .cc or .cpp, .f, .f90 or .f95,²⁰ .m, and .mm, respectively. We recommend using .h for headers, also for C++²¹ or Fortran include files. (Use of extension .C for C++ is no longer supported.) Files in the src directory should not be hidden (start with a dot), and hidden files will under some versions of R be ignored.

¹⁹ either or both of which may not be supported on particular platforms. Their main use is on macOS, but unfortunately recent versions of the macOS SDK have removed much of the support for Objective C v1.0 and Objective C++.

²⁰ This is not accepted by the Intel Fortran compiler.

²¹ Using .hpp is not guaranteed to be portable.

It is not portable (and may not be possible at all) to mix all these languages in a single package. Because R itself uses it, we know that C and fixed-form Fortran can be used together, and mixing C, C++ and Fortran usually work for the platform’s native compilers.

If your code needs to depend on the platform there are certain defines which can be used in C or C++. On all Windows builds (even 64-bit ones) _WIN32 will be defined: on 64-bit Windows builds also _WIN64. For Windows on ARM, test for _M_ARM64 or both _WIN32 and __aarch64__. On macOS __APPLE__ is defined²²; for an ‘Apple Silicon’ platform, test for both __APPLE__ and __arm64__.

²² There is also __APPLE_CC__, but that indicates a compiler with Apple-specific features not the OS, although for historical reasons it is defined by LLVM clang. It is used in Rinlinedfuns.h.

²³ the POSIX terminology, called ‘make variables’ by GNU make.

The default rules can be tweaked by setting macros²³ in a file src/Makevars (see Using Makevars). Note that this mechanism should be general enough to eliminate the need for a package-specific src/Makefile. If such a file is to be distributed, considerable care is needed to make it general enough to work on all R platforms. If it has any targets at all, it should have an appropriate first target named all and a (possibly empty) target clean which removes all files generated by running make (to be used by R CMD INSTALL --clean and R CMD INSTALL --preclean). There are platform-specific file names on Windows: src/Makevars.win takes precedence over src/Makevars and src/Makefile.win must be used. Since R 4.2.0, src/Makevars.ucrt takes precedence over src/Makevars.win and src/Makefile.ucrt takes precedence over src/Makefile.win. src/Makevars.ucrt and src/Makefile.ucrt will be ignored by earlier versions of R, and hence can be used to provide content specific to UCRT or Rtools42 and newer, but the support for .ucrt files may be removed in the future when building packages from source on the older versions of R will no longer be needed, and hence the files may be renamed back to .win. Some make programs require makefiles to have a complete final line, including a newline.

A few packages use the src directory for purposes other than making a shared object (e.g. to create executables). Such packages should have files src/Makefile and src/Makefile.win or src/Makefile.ucrt (unless intended for only Unix-alikes or only Windows). Note that on Unix such makefiles are included after R_HOME/etc/R_ARCH/Makeconf so all the usual R macros and make rules are available – for example C compilation will by default use the C compiler and flags with which R was configured. This also applies on Windows as from R 4.3.0: packages intended to be used with earlier versions should include that file themselves.

The order of inclusion of makefiles for a package which does not have a src/Makefile file is

Unix-alike	Windows
src/Makevars `src/Make	vars.ucrt,src/Makevars.win`
R_HOME/etc/R_ARCH/Makeconf R_HOME/etc/R_ARCH/Makeconf
R_MAKEVARS_SITE, R_HOME/etc/R_ARCH/Makevars.site R_MAKEVARS_SITE, `R_HOME/etc/R_ARCH/Makevars	.site`
R_HOME/share/make/shlib.mk `R_HOME/share/make/win	shlib.mk`
R_MAKEVARS_USER, ~/.R/Makevars-platform, ~/.R/Makevars R_MAKEVARS_USER, ` ~/.R/Makev	ars.ucrt, ~/.R/Makevars.win64, ~/.R/Makevars.win`

For those which do, it is

R_HOME/etc/R_ARCH/Makeconf R_HOME/etc/R_ARCH/Makeconf
R_MAKEVARS_SITE, R_HOME/etc/R_ARCH/Makevars.site R_MAKEVARS_SITE, `R_HOME/etc/R_ARCH/Makevars	.site`
src/Makefile `src/Make	file.ucrt,src/Makefile.win`
R_MAKEVARS_USER, ~/.R/Makevars-platform, ~/.R/Makevars R_MAKEVARS_USER, ` ~/.R/Makev	ars.ucrt, ~/.R/Makevars.win64, ~/.R/Makevars.win`

Items in capitals are environment variables: those separated by commas are alternatives looked for in the order shown.

In very special cases packages may create binary files other than the shared objects/DLLs in the src directory. Such files will not be installed in a multi-architecture setting since R CMD INSTALL --libs-only is used to merge multiple sub-architectures and it only copies shared objects/DLLs. If a package wants to install other binaries (for example executable programs), it should provide an R script src/install.libs.R which will be run as part of the installation in the src build directory instead of copying the shared objects/DLLs. The script is run in a separate R environment containing the following variables: R_PACKAGE_NAME (the name of the package), R_PACKAGE_SOURCE (the path to the source directory of the package), R_PACKAGE_DIR (the path of the target installation directory of the package), R_ARCH (the arch-dependent part of the path, often empty), SHLIB_EXT (the extension of shared objects) and WINDOWS (TRUE on Windows, FALSE elsewhere). Something close to the default behavior could be replicated with the following src/install.libs.R file:

files <- Sys.glob(paste0("*", SHLIB_EXT))
dest <- file.path(R_PACKAGE_DIR, paste0('libs', R_ARCH))
dir.create(dest, recursive = TRUE, showWarnings = FALSE)
file.copy(files, dest, overwrite = TRUE)
if(file.exists("symbols.rds"))
    file.copy("symbols.rds", dest, overwrite = TRUE)

On the other hand, executable programs could be installed along the lines of

execs <- c("one", "two", "three")
if(WINDOWS) execs <- paste0(execs, ".exe")
if ( any(file.exists(execs)) ) {
  dest <- file.path(R_PACKAGE_DIR,  paste0('bin', R_ARCH))
  dir.create(dest, recursive = TRUE, showWarnings = FALSE)
  file.copy(execs, dest, overwrite = TRUE)
}

Note the use of architecture-specific subdirectories of bin where needed. (Executables should installed under a bin directory and not under libs. It is good practice to check that they can be executed as part of the installation script, so a broken package is not installed.)

The data subdirectory is for data files: See Data in packages.

The demo subdirectory is for R scripts (for running via demo()) that demonstrate some of the functionality of the package. Demos may be interactive and are not checked automatically, so if testing is desired use code in the tests directory to achieve this. The script files must start with a (lower or upper case) letter and have one of the extensions .R or .r. If present, the demo subdirectory should also have a 00Index file with one line for each demo, giving its name and a description separated by a tab or at least three spaces. (This index file is not generated automatically.) Note that a demo does not have a specified encoding and so should be an ASCII file (see Encoding issues). Function demo() will use the package encoding if there is one, but this is mainly useful for non-ASCII comments.

The contents of the inst subdirectory will be copied recursively to the installation directory. Subdirectories of inst should not interfere with those used by R (currently, R, data, demo, exec, libs, man, help, html and Meta, and earlier versions used latex, R-ex). The copying of the inst happens after src is built so its Makefile can create files to be installed. To exclude files from being installed, one can specify a list of exclude patterns in file .Rinstignore in the top-level source directory. These patterns should be Perl-like regular expressions (see the help for regexp in R for the precise details), one per line, to be matched case-insensitively against the file and directory paths, e.g. doc/.*[.]png$ will exclude all PNG files in inst/doc based on the extension.

Note that with the exceptions of INDEX, LICENSE/LICENCE and NEWS, information files at the top level of the package will not be installed and so not be known to users of Windows and macOS compiled packages (and not seen by those who use R CMD INSTALL or install.packages() on the tarball). So any information files you wish an end user to see should be included in inst. Note that if the named exceptions also occur in inst, the version in inst will be that seen in the installed package.

Things you might like to add to inst are a CITATION file for use by the citation function, and a NEWS.Rd file for use by the news function. See its help page for the specific format restrictions of the NEWS.Rd file.

Another file sometimes needed in inst is AUTHORS or COPYRIGHTS to specify the authors or copyright holders when this is too complex to put in the DESCRIPTION file.

Subdirectory tests is for additional package-specific test code, similar to the specific tests that come with the R distribution. Test code can either be provided directly in a .R (or .r as from R 3.4.0) file, or via a .Rin file containing code which in turn creates the corresponding .R file (e.g., by collecting all function objects in the package and then calling them with the strangest arguments). The results of running a .R file are written to a .Rout file. If there is a corresponding²⁴ .Rout.save file, these two are compared, with differences being reported but not causing an error. The directory tests is copied to the check area, and the tests are run with the copy as the working directory and with R_LIBS set to ensure that the copy of the package installed during testing will be found by library(pkg_name). Note that the package-specific tests are run in a vanilla R session without setting the random-number seed, so tests which use random numbers will need to set the seed to obtain reproducible results (and it can be helpful to do so in all cases, to avoid occasional failures when tests are run).

²⁴ The best way to generate such a file is to copy the .Rout from a successful run of R CMD check. If you want to generate it separately, do run R with options --vanilla --no-echo and with environment variable LANGUAGE=en set to get messages in English. Be careful not to use output with the option --timings (and note that --as-cran sets it).

If directory tests has a subdirectory Examples containing a file pkg-Ex.Rout.save, this is compared to the output file for running the examples when the latter are checked. Reference output should be produced without having the --timings option set (and note that --as-cran sets it).

If reference output is included for examples, tests or vignettes do make sure that it is fully reproducible, as it will be compared verbatim to that produced in a check run, unless the IGNORE_RDIFF markup is used. Things which trip up maintainers include displayed version numbers from loading other packages, printing numerical results to an unreproducibly high precision and printing timings. Another trap is small values which are in fact rounding error from zero: consider using zapsmall.

Subdirectory exec could contain additional executable scripts the package needs, typically scripts for interpreters such as the shell, Perl, or Tcl. NB: only files (and not directories) under exec are installed (and those with names starting with a dot are ignored), and they are all marked as executable (mode 755, moderated by umask) on POSIX platforms. Note too that this is not suitable for executable programs since some platforms support multiple architectures using the same installed package directory.

Subdirectory po is used for files related to localization: see Internationalization.

Subdirectory tools is the preferred place for auxiliary files needed during configuration, and also for sources need to re-create scripts (e.g. M4 files for autoconf: some prefer to put those in a subdirectory m4 of tools).

1.1.7 Data in packages

The data subdirectory is for data files, either to be made available via lazy-loading or for loading using data(). (The choice is made by the LazyData field in the DESCRIPTION file: the default is not to do so.) It should not be used for other data files needed by the package, and the convention has grown up to use directory inst/extdata for such files.

Data files can have one of three types as indicated by their extension: plain R code (.R or .r), tables (.tab, .txt, or .csv, see ?data for the file formats, and note that .csv is not the standard²⁵ CSV format), or save() images (.RData or .rda). The files should not be hidden (have names starting with a dot). Note that R code should be if possible “self-sufficient” and not make use of extra functionality provided by the package, so that the data file can also be used without having to load the package or its namespace: it should run as silently as possible and not change the search() path by attaching packages or other environments.

²⁵ e.g. https://www.rfc-editor.org/rfc/rfc4180.

²⁶ People who have trouble with case are advised to use .rda as a common error is to refer to abc.RData as abc.Rdata!

Images (extensions .RData²⁶ or .rda) can contain references to the namespaces of packages that were used to create them. Preferably there should be no such references in data files, and in any case they should only be to packages listed in the Depends and Imports fields, as otherwise it may be impossible to install the package. To check for such references, load all the images into a vanilla R session, run str() on all the datasets, and look at the output of loadedNamespaces().

Particular care is needed where a dataset or one of its components is of an S4 class, especially if the class is defined in a different package. First, the package containing the class definition has to be available to do useful things with the dataset, so that package must be listed in Imports or Depends (even if this gives a check warning about unused imports). Second, the definition of an S4 class can change, and often is unnoticed when in a package with a different author. So it may be wiser to use the .R form and use that to create the dataset object when needed (loading package namespaces but not attaching them by using requireNamespace(pkg, quietly = TRUE) and using pkg:: to refer to objects in the namespace).

If you are not using LazyData and either your data files are large or e.g., you use data/foo.R scripts to produce your data, loading your namespace, you can speed up installation by providing a file datalist in the data subdirectory. This should have one line per topic that data() will find, in the format foo if data(foo) provides foo, or foo: bar bah if data(foo) provides bar and bah. R CMD build will automatically add a datalist file to data directories of over 1Mb, using the function tools::add_datalist.

Tables (.tab, .txt, or .csv files) can be compressed by gzip, bzip2 or xz, optionally with additional extension .gz, .bz2 or .xz.

If your package is to be distributed, do consider the resource implications of large datasets for your users: they can make packages very slow to download and use up unwelcome amounts of storage space, as well as taking many seconds to load. It is normally best to distribute large datasets as .rda images prepared by save(, compress = TRUE) (the default). Using bzip2 or xz compression will usually reduce the size of both the package tarball and the installed package, in some cases by a factor of two or more.

Package tools has a couple of functions to help with data images: checkRdaFiles reports on the way the image was saved, and resaveRdaFiles will re-save with a different type of compression, including choosing the best type for that particular image.

Many packages using LazyData will benefit from using a form of compression other than gzip in the installed lazy-loading database. This can be selected by the --data-compress option to R CMD INSTALL or by using the LazyDataCompression field in the DESCRIPTION file. Useful values are bzip2, xz and the default, gzip: value none is also accepted. The only way to discover which is best is to try them all and look at the size of the pkgname/data/Rdata.rdb file. A function to do that (quoting sizes in KB) is

CheckLazyDataCompression <- function(pkg)
{
    pkg_name <- sub("_.*", "", pkg)
    lib <- tempfile(); dir.create(lib)
    zs <- c("gzip", "bzip2", "xz")
    res <- integer(3); names(res) <- zs
    for (z in zs) {
        opts <- c(paste0("--data-compress=", z),
                  "--no-libs", "--no-help", "--no-demo", "--no-exec", "--no-test-load")
        install.packages(pkg, lib, INSTALL_opts = opts, repos = NULL, quiet = TRUE)
        res[z] <- file.size(file.path(lib, pkg_name, "data", "Rdata.rdb"))
    }
    ceiling(res/1024)
}

(applied to a source package without any LazyDataCompression field). R CMD check will warn if it finds a pkgname/data/Rdata.rdb file of more than 5MB without LazyDataCompression being set. If you see that, run CheckLazyDataCompression() and set the field – to gzip in the unlikely event²⁷ that is the best choice.

²⁷ For all the CRAN packages tested, either gz or bzip2 provided a very substantial reduction in installed size.

The analogue for sysdata.rda is field SysDataCompression: the default is xz for files bigger than 1MB otherwise gzip.

Lazy-loading is not supported for very large datasets (those which when serialized exceed 2GB, the limit for the format on 32-bit platforms).

1.1.8 Non-R scripts in packages

Code which needs to be compiled (C, C++, Fortran …) is included in the src subdirectory and discussed elsewhere in this document.

Subdirectory exec could be used for scripts for interpreters such as the shell, BUGS, JavaScript, Matlab, Perl, PHP (amap), Python or Tcl (Simile), or even R. However, it seems more common to use the inst directory, for example WriteXLS/inst/Perl, NMF/inst/m-files, RnavGraph/inst/tcl, RProtoBuf/inst/python and emdbook/inst/BUGS and gridSVG/inst/js.

Java code is a special case: except for very small programs, .java files should be byte-compiled (to a .class file) and distributed as part of a .jar file: the conventional location for the .jar file(s) is inst/java. It is desirable (and required under an Open Source license) to make the Java source files available: this is best done in a top-level java directory in the package—the source files should not be installed.

If your package requires one of these interpreters or an extension then this should be declared in the SystemRequirements field of its DESCRIPTION file. (Users of Java most often do so via rJava, when depending on/importing that suffices unless there is a version requirement on Java code in the package.)

Windows and Mac users should be aware that the Tcl extensions BWidget and Tktable (which have sometimes been included in the Windows²⁸ and macOS R installers) are extensions and do need to be declared (and that Tktable is less widely available than it used to be, including not in the main repositories for major Linux distributions). BWidget needs to be installed by the user on other OSes. This is fairly easy to do: first find the Tcl search path:

²⁸ BWidget still is on Windows but Tktable was not in R 4.0.0.

library(tcltk)
strsplit(tclvalue('auto_path'), " ")[[1]]

then download the sources from https://sourceforge.net/projects/tcllib/files/BWidget/ and in a terminal run something like

tar xf bwidget-1.9.14.tar.gz
sudo mv bwidget-1.9.14 /usr/local/lib

substituting a location on the Tcl search path for /usr/local/lib if needed. (If no location on that search path is writeable, you will need to add one each time BWidget is to be used with tcltk::addTclPath().)

To (silently) test for the presence of Tktable one can use

library(tcltk)
have_tktable <- !isFALSE(suppressWarnings(tclRequire('Tktable')))

Installing Tktable needs a C compiler and the Tk headers (not necessarily installed with Tcl/Tk). At the time of writing the latest sources (from 2008) were available from https://sourceforge.net/projects/tktable/files/tktable/2.10/Tktable2.10.tar.gz/download, but needed patching for current Tk (8.6.11, but not 8.6.10) – a patch can be found at https://www.stats.ox.ac.uk/pub/bdr/Tktable/. For a system installation of Tk you may need to install Tktable as root as on e.g. Fedora all the locations on auto_path are owned by root.

1.1.9 Specifying URLs

URLs in many places in the package documentation will be converted to clickable hyperlinks in at least some of their renderings. So care is needed that their forms are correct and portable.

The full URL should be given, including the scheme (often http:// or https://) and a final / for references to directories.

Spaces in URLs are not portable and how they are handled does vary by HTTP server and by client. There should be no space in the host part of an http:// URL, and spaces in the remainder should be encoded, with each space replaced by %20.

Reserved characters should be encoded unless used in their reserved sense: see the help on URLencode().

The canonical URL for a CRAN package is

https://cran.r-project.org/package=pkgname

and not a version starting https://cran.r-project.org/web/packages/pkgname.

1.2 Configure and cleanup

Note that most of this section is specific to Unix-alikes: see the comments later on about the Windows port of R.

If your package needs some system-dependent configuration before installation you can include an executable (Bourne²⁹ shell script configure in your package which (if present) is executed by R CMD INSTALL before any other action is performed. This can be a script created by the Autoconf mechanism, but may also be a script written by yourself. Use this to detect if any nonstandard libraries are present such that corresponding code in the package can be disabled at install time rather than giving error messages when the package is compiled or used. To summarize, the full power of Autoconf is available for your extension package (including variable substitution, searching for libraries, etc.). Background and useful tips on Autoconf and related tools (including pkg-config described below) can be found at https://autotools.info/.

²⁹ The script should only assume a POSIX-compliant /bin/sh – see https://pubs.opengroup.org/onlinepubs/9699919799/utilities/V3_chap02.html. In particular bash extensions must not be used, and not all R platforms have a bash command, let alone one at /bin/bash. All known shells used with R support the use of backticks, but not all support $(cmd). However, real-world shells are not fully POSIX-compliant and omissions and idiosyncrasies need to be worked around—which Autoconf will do for you. Arithmetic expansion is a known issue: see https://www.gnu.org/software/autoconf/manual/autoconf.html#Portable-Shell for this and others. Some checks can be done by the checkbashisms Perl script at https://sourceforge.net/projects/checkbaskisms/files, also available in most Linux distributions in a package named either devscripts or devscripts-checkbashisms: a later version can be extracted from Debian sources such as the most recent tar.xz in https://deb.debian.org/debian/pool/main/d/devscripts/ and has been needed for recent versions of Perl.

A configure script is run in an environment which has all the environment variables set for an R session (see R_HOME/etc/Renviron) plus R_PACKAGE_NAME (the name of the package), R_PACKAGE_DIR (the path of the target installation directory of the package, a temporary location for staged installs) and R_ARCH (the arch-dependent part of the path, often empty).

Under a Unix-alike only, an executable (Bourne shell) script cleanup is executed as the last thing by R CMD INSTALL if option --clean was given, and by R CMD build when preparing the package for building from its source.

As an example consider we want to use functionality provided by a (C or Fortran) library foo. Using Autoconf, we can create a configure script which checks for the library, sets variable HAVE_FOO to TRUE if it was found and to FALSE otherwise, and then substitutes this value into output files (by replacing instances of @HAVE_FOO@ in input files with the value of HAVE_FOO). For example, if a function named bar is to be made available by linking against library foo (i.e., using -lfoo), one could use

AC_CHECK_LIB(foo, fun, [HAVE_FOO=TRUE], [HAVE_FOO=FALSE])
AC_SUBST(HAVE_FOO)
......
AC_CONFIG_FILES([foo.R])
AC_OUTPUT

in configure.ac (assuming Autoconf 2.50 or later).

The definition of the respective R function in foo.R.in could be

foo <- function(x) {
    if(!@HAVE_FOO@)
      stop("Sorry, library 'foo' is not available")
    ...

From this file configure creates the actual R source file foo.R looking like

foo <- function(x) {
    if(!FALSE)
      stop("Sorry, library 'foo' is not available")
    ...

if library foo was not found (with the desired functionality). In this case, the above R code effectively disables the function.

One could also use different file fragments for available and missing functionality, respectively.

You will very likely need to ensure that the same C compiler and compiler flags are used in the configure tests as when compiling R or your package. Under a Unix-alike, you can achieve this by including the following fragment early in configure.ac (before calling AC_PROG_CC or anything which calls it)

: ${R_HOME=`R RHOME`}
if test -z "${R_HOME}"; then
  echo "could not determine R_HOME"
  exit 1
fi
CC=`"${R_HOME}/bin/R" CMD config CC`
CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS`
CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`

(Using ${R_HOME}/bin/R rather than just R is necessary in order to use the correct version of R when running the script as part of R CMD INSTALL, and the quotes since ${R_HOME} might contain spaces.)

If your code does load checks (for example, to check for an entry point in a library or to run code) then you will also need

LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS`

Packages written with C++ need to pick up the details for the C++ compiler and switch the current language to C++ by something like

CXX=`"${R_HOME}/bin/R" CMD config CXX`
if test -z "$CXX"; then
  AC_MSG_ERROR([No C++ compiler is available])
fi
CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXXFLAGS`
CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`
AC_LANG(C++)

The latter is important, as for example C headers may not be available to C++ programs or may not be written to avoid C++ name-mangling. Note that an R installation is not required to have a C++ compiler so CXX may be empty. If the package specifies a non-default C++ standard, use the config variable names (such as CXX17) appropriate to the standard, but still set CXX and CXXFLAGS.

You can use R CMD config to get the value of the basic configuration variables, and also the header and library flags necessary for linking a front-end executable program against R, see R CMD config --help for details. If you do, it is essential that you use both the command and the appropriate flags, so that for example CC must always be used with CFLAGS and (for code to be linked into a shared library) CPICFLAGS. For Fortran, be careful to use FC FFLAGS FPICFLAGS for fixed-form Fortran and FC FCFLAGS FPICFLAGS for free-form Fortran.

As from R 4.3.0, variables

CC CFLAGS CXX CXXFLAGS CPPFLAGS LDFLAGS FC FCFLAGS

are set in the environment (if not already set) when configure is called from R CMD INSTALL, in case the script forgets to set them as described above. This includes making use of the selected C standard (but not the C++ standard as that is selected at a later stage by R CMD SHLIB).

To check for an external BLAS library using the AX_BLAS macro from the official Autoconf Macro Archive³⁰, one can use

³⁰ https://www.gnu.org/software/autoconf-archive/ax_blas.html. If you include macros from that archive you need to arrange for them to be included in the package sources for use by autoreconf.

FC=`"${R_HOME}/bin/R" CMD config FC`
FCLAGS=`"${R_HOME}/bin/R" CMD config FFLAGS`
AC_PROG_FC
FLIBS=`"${R_HOME}/bin/R" CMD config FLIBS`
AX_BLAS([], AC_MSG_ERROR([could not find your BLAS library], 1))

Note that FLIBS as determined by R must be used to ensure that Fortran code works on all R platforms.

N.B.: If the configure script creates files, e.g. src/Makevars, you do need a cleanup script to remove them. Otherwise R CMD build may ship the files that are created. For example, package RODBC has

#!/bin/sh

rm -f config.* src/Makevars src/config.h

As this example shows, configure often creates working files such as config.log. If you use a hand-crafted script rather than one created by autoconf, it is highly recommended that you log its actions to file config.log.

If your configure script needs auxiliary files, it is recommended that you ship them in a tools directory (as R itself does).

You should bear in mind that the configure script will not be used on Windows systems. If your package is to be made publicly available, please give enough information for a user on a non-Unix-alike platform to configure it manually, or provide a configure.win script (or configure.ucrt) to be used on that platform. (Optionally, there can be a cleanup.win script (or cleanup.ucrt). Both should be shell scripts to be executed by ash, which is a minimal version of Bourne-style sh. As from R 4.2.0, bash is used. When configure.win (or configure.ucrt) is run the environment variables R_HOME (which uses / as the file separator), R_ARCH and R_ARCH_BIN will be set. Use R_ARCH to decide if this is a 64-bit build for Intel (its value there is /x64) and to install DLLs to the correct place (${R_HOME}/libs${R_ARCH}). Use R_ARCH_BIN to find the correct place under the bin directory, e.g. ${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe. If a configure.win script does compilation (including calling R CMD SHLIB), most of the considerations above apply.

As the scripts on Windows are executed as sh ./configure.win and similar, any ‘shebang’ first line (such as #! /bin/bash) is treated as a comment.

In some rare circumstances, the configuration and cleanup scripts need to know the location into which the package is being installed. An example of this is a package that uses C code and creates two shared object/DLLs. Usually, the object that is dynamically loaded by R is linked against the second, dependent, object. On some systems, we can add the location of this dependent object to the object that is dynamically loaded by R. This means that each user does not have to set the value of the LD_LIBRARY_PATH (or equivalent) environment variable, but that the secondary object is automatically resolved. Another example is when a package installs support files that are required at run time, and their location is substituted into an R data structure at installation time. The names of the top-level library directory (i.e., specifiable via the -l argument) and the directory of the package itself are made available to the installation scripts via the two shell/environment variables R_LIBRARY_DIR and R_PACKAGE_DIR. Additionally, the name of the package (e.g. survival or MASS) being installed is available from the environment variable R_PACKAGE_NAME. (Currently the value of R_PACKAGE_DIR is always ${R_LIBRARY_DIR}/${R_PACKAGE_NAME}, but this used not to be the case when versioned installs were allowed. Its main use is in configure.win (or configure.ucrt) scripts for the installation path of external software’s DLLs.) Note that the value of R_PACKAGE_DIR may contain spaces and other shell-unfriendly characters, and so should be quoted in makefiles and configure scripts.

One of the more tricky tasks can be to find the headers and libraries of external software. One tool which is increasingly available on Unix-alikes (but not by default³¹ on macOS) to do this is pkg-config. The configure script will need to test for the presence of the command itself³² (see for example package tiff), and if present it can be asked if the software is installed, of a suitable version and for compilation/linking flags by e.g.

³¹ but it is available on the machines used to produce the CRAN binary packages: however as Apple does not ship .pc files for its system libraries such as expat, libcurl, libxml2, sqlite3 and zlib, it may well not find information on these. Some substitutes are available from https://github.com/R-macos/recipes/tree/master/stubs/pkgconfig-darwin and are installed on the CRAN package builders.

³² It is not wise to check the version of pkg-config as it is sometimes a link to pkgconf, a separate project with a different version series.

$ pkg-config --exists 'libtiff-4 >= 4.1.0' --print-errors # check the status
$ pkg-config --modversion libtiff-4
4.3.0
$ pkg-config --cflags libtiff-4
-I/usr/local/include
$ pkg-config --libs libtiff-4
-L/usr/local/lib -ltiff
$ pkg-config --static --libs libtiff-4
-L/usr/local/lib -ltiff -lwebp -llzma -ljpeg -lz

Note that pkg-config --libs gives the information required to link against the default version³³ of that library (usually the dynamic one), and pkg-config --static --libs may be needed if the static library is to be used.

³³ but not all projects get this right when only a static library is installed, so it is often necessary to try in turn pkg-config --libs and pkg-config --static --libs.

Static libraries are commonly used on macOS and Windows to facilitate bundling external software with binary distributions of packages. This means that portable (source) packages need to allow for this. It is not safe to just use pkg-config --static --libs, as that will often include further libraries that are not necessarily installed on the user’s system (or maybe only the versioned library such as libjbig.so.2.1 is installed and not libjbig.so which would be needed to use -ljbig sometimes included in pkg-config --static --libs libtiff-4).

Another issue is that pkg-config --exists may not be reliable. It checks not only that the ‘module’ is available but all of the dependencies, including those in principle needed for static linking. (XQuartz 2.8.x only distributed dynamic libraries and not some of the .pc files needed for --exists.)

Sometimes the name by which the software is known to pkg-config is not what one might expect (e.g. libxml-2.0 even for 2.9.x). To get a complete list use

pkg-config --list-all | sort

Some external software provides a -config command to do a similar job to pkg-config, including

curl-config   freetype-config  gdal-config    geos-config
gsl-config    iodbc-config     libpng-config  nc-config
pcre-config   pcre2-config     xml2-config    xslt-config

(curl-config is for libcurl not curl. nc-config is for netcdf.) Most have an option to use static libraries.

N.B. These commands indicate what header paths and libraries are needed, but they do not obviate the need to check that the recipes they give actually work. (This is especially necessary for platforms which use static linking.)

If using Autoconf it is good practice to include all the Autoconf sources in the package (and required for an Open Source package and tested by R CMD check --as-cran). This will include the file configure.ac³⁴ in the top-level directory of the package. If extensions written in m4 are needed, these should be included under the directory tools and included from configure.ac via e.g.,

³⁴ a decade ago Autoconf used configure.in: this is still accepted but should be renamed and autoreconf as used by R CMD check --as-cran will report as such.

m4_include([tools/ax_pthread.m4])

Alternatively, Autoconf can be asked to search all .m4 files in a directory by including something like³⁵

³⁵ For those using autoconf 2.70 or later there is also AC_CONFIG_MACRO_DIRS which allows multiple directories to be specified.

AC_CONFIG_MACRO_DIR([tools/m4])

One source of such extensions is the ‘Autoconf Archive’ (https://www.gnu.org/software/autoconf-archive/. It is not safe to assume this is installed on users’ machines, so the extension should be shipped with the package (taking care to comply with its licence).

1.2.1 Using Makevars

Sometimes writing your own configure script can be avoided by supplying a file Makevars: also one of the most common uses of a configure script is to make Makevars from Makevars.in.

A Makevars file is a makefile and is used as one of several makefiles by R CMD SHLIB (which is called by R CMD INSTALL to compile code in the src directory). It should be written if at all possible in a portable style, in particular (except for Makevars.win and Makevars.ucrt) without the use of GNU extensions.

The most common use of a Makevars file is to set additional preprocessor options (for example include paths and definitions) for C/C++ files via PKG_CPPFLAGS, and additional compiler flags by setting PKG_CFLAGS, PKG_CXXFLAGS or PKG_FFLAGS, for C, C++ or Fortran respectively (see Creating shared objects).

N.B.: Include paths are preprocessor options, not compiler options, and must be set in PKG_CPPFLAGS as otherwise platform-specific paths (e.g. -I/usr/local/include) will take precedence. PKG_CPPFLAGS should contain -I, -D, -U and (where supported) -include and -pthread options: everything else should be a compiler flag. The order of flags matters, and using -I in PKG_CFLAGS or PKG_CXXFLAGS has led to hard-to-debug platform-specific errors.

Makevars can also be used to set flags for the linker, for example -L and -l options, via PKG_LIBS.

When writing a Makevars file for a package you intend to distribute, take care to ensure that it is not specific to your compiler: flags such as -O2 -Wall -pedantic (and all other -W flags: for the Oracle compilers these were used to pass arguments to compiler phases) are all specific to GCC (and compilers such as clang which aim to be options-compatible with it).

Also, do not set variables such as CPPFLAGS, CFLAGS etc.: these should be settable by users (sites) through appropriate personal (site-wide) Makevars files. See Customizing package compilation in R Installation and Administration for more information.

There are some macros³⁶ which are set whilst configuring the building of R itself and are stored in R_HOME/etcR_ARCH/Makeconf. That makefile is included as a Makefile after Makevars[.win], and the macros it defines can be used in macro assignments and make command lines in the latter. These include

³⁶ in POSIX parlance: GNU make calls these ‘make variables’.

FLIBS ¶

A macro containing the set of libraries need to link Fortran code. This may need to be included in PKG_LIBS: it will normally be included automatically if the package contains Fortran source files in the src directory.

BLAS_LIBS ¶

A macro containing the BLAS libraries used when building R. This may need to be included in PKG_LIBS. Beware that if it is empty then the R executable will contain all the double-precision and double-complex BLAS routines, but no single-precision nor complex routines. If BLAS_LIBS is included, then FLIBS also needs to be³⁷ included following it, as most BLAS libraries are written at least partially in Fortran. However, it can be omitted if the package contains Fortran source code as that will add FLIBS to the link line.

³⁷ at least on Unix-alikes: the Windows build currently resolves such dependencies to a static Fortran library when Rblas.dll is built.

LAPACK_LIBS ¶

A macro containing the LAPACK libraries (and paths where appropriate) used when building R. This may need to be included in PKG_LIBS. It may point to a dynamic library libRlapack which contains the main double-precision LAPACK routines as well as those double-complex LAPACK routines needed to build R, or it may point to an external LAPACK library, or may be empty if an external BLAS library also contains LAPACK.

[libRlapack includes all the double-precision LAPACK routines which were current in 2003 and a few more recent ones: a list of which routines are included is in file src/modules/lapack/README. Note that an external LAPACK/BLAS library need not do so, as some were ‘deprecated’ (and not compiled by default) in LAPACK 3.6.0 in late 2015.]

For portability, the macros BLAS_LIBS and FLIBS should always be included after LAPACK_LIBS (and in that order).

SAFE_FFLAGS ¶

A macro containing flags which are needed to circumvent over-optimization of FORTRAN code: it is might be -g -O2 -ffloat-store or -g -O2 -msse2 -mfpmath=sse on ix86 platforms using gfortran. Note that this is not an additional flag to be used as part of PKG_FFLAGS, but a replacement for FFLAGS. See the example later in this section.

Setting certain macros in Makevars will prevent R CMD SHLIB setting them: in particular if Makevars sets OBJECTS it will not be set on the make command line. This can be useful in conjunction with implicit rules to allow other types of source code to be compiled and included in the shared object. It can also be used to control the set of files which are compiled, either by excluding some files in src or including some files in subdirectories. For example

OBJECTS = 4dfp/endianio.o 4dfp/Getifh.o R4dfp-object.o

Note that Makevars should not normally contain targets, as it is included before the default makefile and make will call the first target, intended to be all in the default makefile. If you really need to circumvent that, use a suitable (phony) target all before any actual targets in Makevars.[win]: for example package fastICA used to have

PKG_LIBS = @BLAS_LIBS@

SLAMC_FFLAGS=$(R_XTRA_FFLAGS) $(FPICFLAGS) $(SHLIB_FFLAGS) $(SAFE_FFLAGS)

all: $(SHLIB)

slamc.o: slamc.f
        $(FC) $(SLAMC_FFLAGS) -c -o slamc.o slamc.f

needed to ensure that the LAPACK routines find some constants without infinite looping. The Windows equivalent was

all: $(SHLIB)

slamc.o: slamc.f
        $(FC) $(SAFE_FFLAGS) -c -o slamc.o slamc.f

(since the other macros are all empty on that platform, and R’s internal BLAS was not used). Note that the first target in Makevars will be called, but for back-compatibility it is best named all.

If you want to create and then link to a library, say using code in a subdirectory, use something like

.PHONY: all mylibs

all: $(SHLIB)
$(SHLIB): mylibs

mylibs:
        (cd subdir; $(MAKE))

Be careful to create all the necessary dependencies, as there is no guarantee that the dependencies of all will be run in a particular order (and some of the CRAN build machines use multiple CPUs and parallel makes). In particular,

all: mylibs

does not suffice. GNU make does allow the construct

.NOTPARALLEL: all
all: mylibs $(SHLIB)

but that is not portable. dmake and pmake allow the similar .NO_PARALLEL, also not portable: some variants of pmake accept .NOTPARALLEL as an alias for .NO_PARALLEL.

Note that on Windows it is required that Makevars[.win, .ucrt] does create a DLL: this is needed as it is the only reliable way to ensure that building a DLL succeeded. If you want to use the src directory for some purpose other than building a DLL, use a Makefile.win or Makefile.ucrt file.

It is sometimes useful to have a target clean in Makevars, Makevars.win or Makevars.ucrt: this will be used by R CMD build to clean up (a copy of) the package sources. When it is run by build it will have fewer macros set, in particular not $(SHLIB), nor $(OBJECTS) unless set in the file itself. It would also be possible to add tasks to the target shlib-clean which is run by R CMD INSTALL and R CMD SHLIB with options --clean and --preclean.

Avoid the use of default (also known as ‘implicit’ rules) in makefiles, as these are make-specific. Even when mandated by POSIX – GNU make does not comply and this has broken package installation.

An unfortunately common error is to have

all: $(SHLIB) clean

which asks make to clean in parallel with compiling the code. Not only does this lead to hard-to-debug installation errors, it wipes out all the evidence of any error (from a parallel make or not). It is much better to leave cleaning to the end user using the facilities in the previous paragraph.

If you want to run R code in Makevars, e.g. to find configuration information, please do ensure that you use the correct copy of R or Rscript: there might not be one in the path at all, or it might be the wrong version or architecture. The correct way to do this is via

"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" filename
"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e 'R expression'

where $(R_ARCH_BIN) is only needed currently on Windows.

Environment or make variables can be used to select different macros for Intel 64-bit code or code for other architectures, for example (GNU make syntax, allowed on Windows)

ifeq "$(WIN)" "64"
PKG_LIBS = value for 64-bit Intel Windows
else
PKG_LIBS = value for unknown Windows architectures
endif

On Windows there is normally a choice between linking to an import library or directly to a DLL. Where possible, the latter is much more reliable: import libraries are tied to a specific toolchain, and in particular on 64-bit Windows two different conventions have been commonly used. So for example instead of

PKG_LIBS = -L$(XML_DIR)/lib -lxml2

one can use

PKG_LIBS = -L$(XML_DIR)/bin -lxml2

since on Windows -lxxx will look in turn for

libxxx.dll.a
xxx.dll.a
libxxx.a
xxx.lib
libxxx.dll
xxx.dll

where the first and second are conventionally import libraries, the third and fourth often static libraries (with .lib intended for Visual C++), but might be import libraries. See for example https://sourceware.org/binutils/docs-2.20/ld/WIN32.html#WIN32.

The fly in the ointment is that the DLL might not be named libxxx.dll, and in fact on 32-bit Windows there was a libxml2.dll whereas on one build for 64-bit Windows the DLL is called libxml2-2.dll. Using import libraries can cover over these differences but can cause equal difficulties.

If static libraries are available they can save a lot of problems with run-time finding of DLLs, especially when binary packages are to be distributed and even more when these support both architectures. Where using DLLs is unavoidable we normally arrange (via configure.win or configure.ucrt) to ship them in the same directory as the package DLL.

1.2.2 OpenMP support

There is some support for packages which wish to use OpenMP³⁸. The make macros

³⁸ https://www.openmp.org/, https://en.wikipedia.org/wiki/OpenMP, https://hpc-tutorials.llnl.gov/openmp/

SHLIB_OPENMP_CFLAGS
SHLIB_OPENMP_CXXFLAGS
SHLIB_OPENMP_FFLAGS

are available for use in src/Makevars, src/Makevars.win or Makevars.ucrt. Include the appropriate macro in PKG_CFLAGS, PKG_CXXFLAGS and so on, and also in PKG_LIBS (but see below for Fortran). C/C++ code that needs to be conditioned on the use of OpenMP can be used inside #ifdef _OPENMP: note that some toolchains used for R (including Apple’s for macOS³⁹ and some others using clang⁴⁰) have no OpenMP support at all, not even omp.h.

³⁹ There are somewhat fragile workarounds: see https://mac.r-project.org/openmp/.

⁴⁰ Default builds of LLVM clang 3.8.0 and later have support for OpenMP, but the libomp run-time library may not be installed.

For example, a package with C code written for OpenMP should have in src/Makevars the lines

PKG_CFLAGS = $(SHLIB_OPENMP_CFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CFLAGS)

Note that the macro SHLIB_OPENMP_CXXFLAGS applies to the default C++ compiler and not necessarily to the C++17/20/23 compiler: users of the latter should do their own configure checks. If you do use your own checks, make sure that OpenMP support is complete by compiling and linking an OpenMP-using program: on some platforms the runtime library is optional and on others that library depends on other optional libraries.

Some care is needed when compilers are from different families which may use different OpenMP runtimes (e.g. clang vs GCC including gfortran, although it is often possible to use the clang runtime with GCC but not vice versa: however gfortran >= 9 may generate calls not in the clang runtime). For a package with Fortran code using OpenMP the appropriate lines are

PKG_FFLAGS = $(SHLIB_OPENMP_FFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CFLAGS)

as the C compiler will be used to link the package code. There are platforms on which this does not work for some OpenMP-using code and installation will fail. Since R >= 3.6.2 the best alternative for a package with only Fortran sources using OpenMP is to use

USE_FC_TO_LINK =
PKG_FFLAGS = $(SHLIB_OPENMP_FFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_FFLAGS)

in src/Makevars, src/Makevars.win or Makevars.ucrt. Note however, that when this is used $(FLIBS) should not be included in PKG_LIBS since it is for linking Fortran-compiled code by the C compiler.

Common platforms may inline all OpenMP calls and so tolerate the omission of the OpenMP flag from PKG_LIBS, but this usually results in an installation failure with a different compiler or compilation flags. So cross-check that e.g. -fopenmp appears in the linking line in the installation logs.

It is not portable to use OpenMP with more than one of C, C++ and Fortran in a single package since it is not uncommon that the compilers are of different families.

For portability, any C/C++ code using the omp_* functions should include the omp.h header: some compilers (but not all) include it when OpenMP mode is switched on (e.g. via flag -fopenmp).

There is nothing⁴¹ to say what version of OpenMP is supported: version 4.0 (and much of 4.5 or 5.0) is supported by recent versions of the Linux and Windows platforms, but portable packages cannot assume that end users have recent versions. Apple clang on macOS has no OpenMP support. https://www.openmp.org/resources/openmp-compilers-tools/ gives some idea of what compilers support what versions. Note that support for Fortran compilers is often less up-to-date and that page suggests it is unwise to rely on a version later than 3.1. Which introduced a Fortran OpenMP module, so Fortran users of OpenMP should include

⁴¹ In most implementations the _OPENMP macro has value a date which can be mapped to an OpenMP version: for example, value 201307 is the date of version 4.0 (July 2013). However this may be used to denote the latest version which is partially supported, not that which is fully implemented.

use omp_lib

Rarely, using OpenMP with clang on Linux generates calls in libatomic, resulting in loading messages like

  undefined symbol: __atomic_compare_exchange
  undefined symbol: __atomic_load

The workaround is to link with -latomic (having checked it exists).

The performance of OpenMP varies substantially between platforms. The Windows implementation has substantial overheads, so is only beneficial if quite substantial tasks are run in parallel. Also, on Windows new threads are started with the default⁴² FPU control word, so computations done on OpenMP threads will not make use of extended-precision arithmetic which is the default for the main process.

⁴² Windows default, not MinGW-w64 default.

Do not include these macros unless your code does make use of OpenMP (possibly for C++ via included external headers): this can result in the OpenMP runtime being linked in, threads being started, ….

Calling any of the R API from threaded code is ‘for experts only’ and strongly discouraged. Many functions in the R API modify internal R data structures and might corrupt these data structures if called simultaneously from multiple threads. Most R API functions can signal errors, which must only happen on the R main thread. Also, external libraries (e.g. LAPACK) may not be thread-safe.

Packages are not standard-alone programs, and an R process could contain more than one OpenMP-enabled package as well as other components (for example, an optimized BLAS) making use of OpenMP. So careful consideration needs to be given to resource usage. OpenMP works with parallel regions, and for most implementations the default is to use as many threads as ‘CPUs’ for such regions. Parallel regions can be nested, although it is common to use only a single thread below the first level. The correctness of the detected number of ‘CPUs’ and the assumption that the R process is entitled to use them all are both dubious assumptions. One way to limit resources is to limit the overall number of threads available to OpenMP in the R process: this can be done via environment variable OMP_THREAD_LIMIT, where implemented.⁴³ Alternatively, the number of threads per region can be limited by the environment variable OMP_NUM_THREADS or API call omp_set_num_threads, or, better, for the regions in your code as part of their specification. E.g. R uses⁴⁴

⁴³ Which it was at the time of writing with GCC, Intel and Clang compilers. The count may include the thread running the main process.

⁴⁴ Be careful not to declare nthreads as const int: the Oracle compiler required it to be ‘an lvalue’.

#pragma omp parallel for num_threads(nthreads) ...

That way you only control your own code and not that of other OpenMP users.

Note that setting environment variables to control OpenMP is implementation-dependent and may need to be done outside the R process or before any use of OpenMP (which might be by another process or R itself). Also, implementation-specific variables such as KMP_THREAD_LIMIT might take precedence.

1.2.3 Using pthreads

There is no direct support for the POSIX threads (more commonly known as pthreads): by the time we considered adding it several packages were using it unconditionally so it seems that nowadays it is universally available on POSIX operating systems.

For reasonably recent versions of gcc and clang the correct specification is

PKG_CPPFLAGS = -pthread
PKG_LIBS = -pthread

(and the plural version is also accepted on some systems/versions). For other platforms the specification is

PKG_CPPFLAGS = -D_REENTRANT
PKG_LIBS = -lpthread

(and note that the library name is singular). This is what -pthread does on all known current platforms (although earlier versions of OpenBSD used a different library name).

For a tutorial see https://hpc-tutorials.llnl.gov/posix/.

POSIX threads are not normally used on Windows which has its own native concepts of threads: however, recent toolchains do provide the pthreads header and library.

The presence of a working pthreads implementation cannot be unambiguously determined without testing for yourself: however, that _REENTRANT is defined in C/C++ code is a good indication.

Note that not all pthreads implementations are equivalent as parts are optional (see https://pubs.opengroup.org/onlinepubs/009695399/basedefs/pthread.h.html): for example, macOS lacks the ‘Barriers’ option.

See also the comments on thread-safety and performance under OpenMP: on all known R platforms OpenMP is implemented via pthreads and the known performance issues are in the latter.

1.2.4 Compiling in sub-directories

Package authors fairly often want to organize code in sub-directories of src, for example if they are including a separate piece of external software to which this is an R interface.

One simple way is simply to set OBJECTS to be all the objects that need to be compiled, including in sub-directories. For example, CRAN package RSiena has

SOURCES = $(wildcard data/*.cpp network/*.cpp utils/*.cpp model/*.cpp model/*/*.cpp model/*/*/*.cpp)

OBJECTS = siena07utilities.o siena07internals.o siena07setup.o siena07models.o $(SOURCES:.cpp=.o)

One problem with that approach is that unless GNU make extensions are used, the source files need to be listed and kept up-to-date. As in the following from CRAN package lossDev:

OBJECTS.samplers = samplers/ExpandableArray.o samplers/Knots.o \
  samplers/RJumpSpline.o samplers/RJumpSplineFactory.o \
  samplers/RealSlicerOV.o samplers/SliceFactoryOV.o samplers/MNorm.o
OBJECTS.distributions = distributions/DSpline.o \
  distributions/DChisqrOV.o distributions/DTOV.o \
  distributions/DNormOV.o distributions/DUnifOV.o distributions/RScalarDist.o
OBJECTS.root = RJump.o

OBJECTS = $(OBJECTS.samplers) $(OBJECTS.distributions) $(OBJECTS.root)

Where the subdirectory is self-contained code with a suitable makefile, the best approach is something like

PKG_LIBS = -LCsdp/lib -lsdp $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)

$(SHLIB): Csdp/lib/libsdp.a

Csdp/lib/libsdp.a:
        @(cd Csdp/lib && $(MAKE) libsdp.a \
          CC="$(CC)" CFLAGS="$(CFLAGS) $(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)")

Note the quotes: the macros can contain spaces, e.g. CC = "gcc -m64 -std=gnu99". Several authors have forgotten about parallel makes: the static library in the subdirectory must be made before the shared object ($(SHLIB)) and so the latter must depend on the former. Others forget the need⁴⁵ for position-independent code.

⁴⁵ A few OSes (AIX, Windows) do not need special flags for such code, but most do—although compilers will often generate PIC code when not asked to do so.

We really do not recommend using src/Makefile instead of src/Makevars, and as the example above shows, it is not necessary.

1.2.5 Configure example

It may be helpful to give an extended example of using a configure script to create a src/Makevars file: this is based on that in the RODBC package.

The configure.ac file follows: configure is created from this by running autoconf in the top-level package directory (containing configure.ac).

AC_INIT([RODBC], 1.1.8) dnl package name, version

dnl A user-specifiable option
odbc_mgr=""
AC_ARG_WITH([odbc-manager],
            AC_HELP_STRING([--with-odbc-manager=MGR],
                           [specify the ODBC manager, e.g. odbc or iodbc]),
            [odbc_mgr=$withval])

if test "$odbc_mgr" = "odbc" ; then
  AC_PATH_PROGS(ODBC_CONFIG, odbc_config)
fi

dnl Select an optional include path, from a configure option
dnl or from an environment variable.
AC_ARG_WITH([odbc-include],
            AC_HELP_STRING([--with-odbc-include=INCLUDE_PATH],
                           [the location of ODBC header files]),
            [odbc_include_path=$withval])
RODBC_CPPFLAGS="-I."
if test [ -n "$odbc_include_path" ] ; then
   RODBC_CPPFLAGS="-I. -I${odbc_include_path}"
else
  if test [ -n "${ODBC_INCLUDE}" ] ; then
     RODBC_CPPFLAGS="-I. -I${ODBC_INCLUDE}"
  fi
fi

dnl ditto for a library path
AC_ARG_WITH([odbc-lib],
            AC_HELP_STRING([--with-odbc-lib=LIB_PATH],
                           [the location of ODBC libraries]),
            [odbc_lib_path=$withval])
if test [ -n "$odbc_lib_path" ] ; then
   LIBS="-L$odbc_lib_path ${LIBS}"
else
  if test [ -n "${ODBC_LIBS}" ] ; then
     LIBS="-L${ODBC_LIBS} ${LIBS}"
  else
    if test -n "${ODBC_CONFIG}"; then
      odbc_lib_path=`odbc_config --libs | sed s/-lodbc//`
      LIBS="${odbc_lib_path} ${LIBS}"
    fi
  fi
fi

dnl Now find the compiler and compiler flags to use
: ${R_HOME=`R RHOME`}
if test -z "${R_HOME}"; then
  echo "could not determine R_HOME"
  exit 1
fi
CC=`"${R_HOME}/bin/R" CMD config CC`
CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS`
CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`

if test -n "${ODBC_CONFIG}"; then
  RODBC_CPPFLAGS=`odbc_config --cflags`
fi
CPPFLAGS="${CPPFLAGS} ${RODBC_CPPFLAGS}"

dnl Check the headers can be found
AC_CHECK_HEADERS(sql.h sqlext.h)
if test "${ac_cv_header_sql_h}" = no ||
   test "${ac_cv_header_sqlext_h}" = no; then
   AC_MSG_ERROR("ODBC headers sql.h and sqlext.h not found")
fi

dnl search for a library containing an ODBC function
if test [ -n "${odbc_mgr}" ] ; then
  AC_SEARCH_LIBS(SQLTables, ${odbc_mgr}, ,
      AC_MSG_ERROR("ODBC driver manager ${odbc_mgr} not found"))
else
  AC_SEARCH_LIBS(SQLTables, odbc odbc32 iodbc, ,
      AC_MSG_ERROR("no ODBC driver manager found"))
fi

dnl for 64-bit ODBC need SQL[U]LEN, and it is unclear where they are defined.
AC_CHECK_TYPES([SQLLEN, SQLULEN], , , [# include <sql.h>])
dnl for unixODBC header
AC_CHECK_SIZEOF(long, 4)

dnl substitute RODBC_CPPFLAGS and LIBS
AC_SUBST(RODBC_CPPFLAGS)
AC_SUBST(LIBS)
AC_CONFIG_HEADERS([src/config.h])
dnl and do substitution in the src/Makevars.in and src/config.h
AC_CONFIG_FILES([src/Makevars])
AC_OUTPUT

where src/Makevars.in would be simply

PKG_CPPFLAGS = @RODBC_CPPFLAGS@
PKG_LIBS = @LIBS@

A user can then be advised to specify the location of the ODBC driver manager files by options like (lines broken for easier reading)

R CMD INSTALL \
  --configure-args='--with-odbc-include=/opt/local/include \
  --with-odbc-lib=/opt/local/lib --with-odbc-manager=iodbc' \
  RODBC

or by setting the environment variables ODBC_INCLUDE and ODBC_LIBS.

1.2.6 Using modern Fortran code

R assumes that source files with extension .f are fixed-form Fortran 90 (which includes Fortran 77), and passes them to the compiler specified by macro FC. The Fortran compiler will also accept free-form Fortran 90/95 code with extension .f90 or (most⁴⁶) .f95.

⁴⁶ Intel compilers do not by default but this is worked around when using packages without a src/Makefile.

The same compiler is used for both fixed-form and free-form Fortran code (with different file extensions and possibly different flags). Macro PKG_FFLAGS can be used for package-specific flags: for the un-encountered case that both are included in a single package and that different flags are needed for the two forms, macro PKG_FCFLAGS is also available for free-form Fortran.

The code used to build R allows a ‘Fortran 90’ compiler to be selected as FC, so platforms might be encountered which only support Fortran 90. However, Fortran 95 is supported on all known platforms.

Most compilers specified by FC will accept most Fortran 2003, 2008 or 2018 code: such code should still use file extension .f90. Most current platforms use gfortran where you might need to include -std=f2003, -std=f2008 or (from version 8) -std=f2018 in PKG_FFLAGS or PKG_FCFLAGS: the default is ‘GNU Fortran’, currently Fortran 2018 (but Fortran 95 prior to gfortran 8) with non-standard extensions. The other compilers in current use (LLVM’s flang-new and Intel’s ifx) default to Fortran 2018⁴⁷.

⁴⁷ but was said to have complete support only from version 2023.0.0.

It is good practice to describe a Fortran version requirement in DESCRIPTIONs SystemRequirements field. Note that this is purely for information: the package also needs a configure script to determine the compiler and set appropriate option(s) and test that the features needed from the standard are actually supported.

The Fortran 2023 released in Nov 2023: as usual compiler vendors are introducing support incrementally. For Intel’s ifx see https://www.intel.com/content/www/us/en/developer/articles/technical/fortran-language-and-openmp-features-in-ifx.html#Fortran%20Standards. For LLVM’s flang-new see https://flang.llvm.org/docs/F202X.html. gfortran does not have complete support even for the 2008 and 2018 standards, but the option -std=f2023 is supported from version 14.1.

Modern versions of Fortran support modules, whereby compiling one source file creates a module file which is then included in others. (Module files typically have a .mod extension: they do depend on the compiler used and so should never be included in a package.) This creates a dependence which make will not know about and often causes installation with a parallel make to fail. Thus it is necessary to add explicit dependencies to src/Makevars to tell make the constraints on the order of compilation. For example, if file iface.f90 creates a module iface used by files cmi.f90 and dmi.f90 then src/Makevars needs to contain something like

cmi.o dmi.o: iface.o

Some maintainers have found it difficult to find all the module dependencies which leads to hard-to-reproduce installation failures. There are tools available to find these, including the Intel compiler’s flag -gen-dep and makedepf90.

Note that it is not portable (although some platforms do accept it) to define a module of the same name in multiple source files.

1.2.7 Using C++ code

R can be built without a C++ compiler although one is available (but not necessarily installed) on all known R platforms. As from R 4.0.0 a C++ compiler will be selected only if it conforms to the 2011 standard (‘C++11’). A minor update⁴⁸ (‘C++14’) was published in December 2014 and was used by default as from R 4.1.0 if supported. Further revisions ‘C++17’ (in December 2017) and ‘C++20’ (with many new features in December 2020) have been published since. The next revision, ‘C++23’, is expected in 2024 and several compilers already have extensive partial support for the final draft.

⁴⁸ Some changes are linked from https://isocpp.org/std/standing-documents/sd-6-sg10-feature-test-recommendations: there were also additional deprecations.

The default standard for compiling R packages was changed to C++17 in R 4.3.0 if supported, and from R 4.4.0 only a C++17 compiler will be selected as the default C++ compiler.

What standard a C++ compiler aims to support can be hard to determine: the value⁴⁹ of __cplusplus may help but some compilers use it to denote a standard which is partially supported and some the latest standard which is (almost) fully supported. On a Unix-alike configure will try to identify a compiler and flags for each of the standards: this relies heavily on the reported values of __cplusplus.

⁴⁹ Values 201103L, 201402L, 201703L and 202002L are most commonly used for C++11, C++14, C++17 and C++20 respectively, but some compilers set 1L. For C++23 all that can currently be assumed is a value greater than that for C++20: for example g++ 12 uses 202100L and clang++ (LLVM 15, Apple 14) uses 202101L.

The webpage https://en.cppreference.com/w/cpp/compiler_support gives some information on which compilers are known to support recent C++ features.

C++ standards have deprecated and later removed features. Be aware that some current compilers still accept removed features in C++17 mode, such as std::unary_function (deprecated in C++11, removed in C++17).

Different versions of R have used different default C++ standards, so for maximal portability a package should specify the standard it requires. In order to specify C++14 code in a package with a Makevars file (or Makevars.win or Makevars.ucrt on Windows) should include the line

CXX_STD = CXX14

Compilation and linking will then be done with the C++14 compiler (if any). Analogously for other standards (details below). On the other hand, specifying C++11⁵⁰ when the code is valid under C++14 or C++17 reduces future portability.

⁵⁰ Often historically used to mean ‘not C++98’

Packages without a src/Makevars or src/Makefile file may specify a C++ standard for code in the src directory by including something like C++14 in the SystemRequirements field of the DESCRIPTION file, e.g.

SystemRequirements: C++14

If a package does have a src/Makevars[.win] file then also setting the make variable CXX_STD there is recommended, as it allows R CMD SHLIB to work correctly in the package’s src directory.

A requirement of C++17 or later should always be declared in the SystemRequirements field (as well as in src/Makevars or src/Makefile) so this is shown on the package’s summary pages on CRAN or similar. This is also good practice for a requirement of C++14. Note that support of C++14 or C++17 is only available from R 3.4.0, so if the package has an R version requirement it needs to take that into account.

Essentially complete C++14 support is available from GCC 5, LLVM clang 3.4 and currently-used versions of Apple clang.

Code needing C++14 features can check for their presence via ‘SD-6 feature tests’⁵¹. Such a check could be

⁵¹ See https://isocpp.org/std/standing-documents/sd-6-sg10-feature-test-recommendations or https://en.cppreference.com/w/cpp/experimental/feature_test. It seems a reasonable assumption that any compiler promising some C++14 conformance will provide these—e.g. g++ 4.9.x did but 4.8.5 did not.

#include <memory> // header where this is defined
#if defined(__cpp_lib_make_unique) && (__cpp_lib_make_unique >= 201304)
using std::make_unique;
#else
// your emulation
#endif

C++17 (as from R 3.4.0), C++20 (as from R 4.0.0) and C++23 (as from R 4.3.0) can be specified in an analogous way (replacing 14 by 17, 20 or 23) but compiler/OS support is platform-dependent. Some C++17 and C++20 support is available with the default builds of R on macOS and Windows as from R 4.0.0. Much of g++s support for C++17 needs version 7 or later: that is more recent than some still-current Linux distributions but often packages for later compilers are available: for RHEL/Centos 7 look for ‘devtoolset’.

Note that C++17 or later ‘support’ does not mean complete support: use feature tests as well as resources such as https://en.cppreference.com/w/cpp/compiler_support, https://gcc.gnu.org/projects/cxx-status.html and https://clang.llvm.org/cxx_status.html to see if the features you want to use are widely implemented. In particular, for C++23 R’s configure script only checks for a compiler claiming to be later than C++20.⁵²

⁵² The __cplusplus value for C++23 is not yet finalized and all but some of the latest compilers use values from earlier drafts.

Attempts to specify an unknown C++ standard are silently ignored: recent versions of R throw an error for C++98 and for known standards for which no compiler+flags has been detected.

If a package using C++ has a configure script it is essential that the script selects the correct C++ compiler and standard, via something like

CXX17=`"${R_HOME}/bin/R" CMD config CXX17`
if test -z "$CXX17"; then
  AC_MSG_ERROR([No C++17 compiler is available])
fi
CXX17STD=`"${R_HOME}/bin/R" CMD config CXX17STD`
CXX="${CXX17} ${CXX17STD}"
CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXX17FLAGS`
## for an configure.ac file
AC_LANG(C++)

if C++17 was specified, but using

CXX=`"${R_HOME}/bin/R" CMD config CXX`
CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXXFLAGS`
## for an configure.ac file
AC_LANG(C++)

if no standard was specified.

If you want to compile C++ code in a subdirectory, make sure you pass down the macros to specify the appropriate compiler, e.g. in src/Makevars

sublibs:
         @(cd libs && $(MAKE) \
            CXX="$(CXX17) $(CXX17STD)" CXXFLAGS="$(CXX17FLAGS) $(CXX17PICFLAGS)")

The discussion above is about the standard R ways of compiling C++: it will not apply to packages using src/Makefile or building in a subdirectory that do not set the C++ standard. And compilers’ default C++ standards varies widely and gets changed frequently by vendors – for example Apple clang up to at least 16 defaults to C++98, LLVM clang 14–15 to C++14, LLVM clang 16–18 to C++17 and g++ 11–14 to C++17.

For a package with a src/Makefile (or a Windows analogue), a non-default C++ compiler can be selected by including something like

CXX14 = `"${R_HOME}/bin/R" CMD config CXX14`
CXX14STD = `"${R_HOME}/bin/R" CMD config CXX14STD`
CXX = ${CXX14} ${CXX14STD}
CXXFLAGS = `"${R_HOME}/bin/R" CMD config CXX14FLAGS`
CXXPICFLAGS = `"${R_HOME}/bin/R" CMD config CXX14PICFLAGS`
SHLIB_LD = "${R_HOME}/bin/R" CMD config SHLIB_CXX14LD`
SHLIB_LDFLAGS = "${R_HOME}/bin/R" CMD config SHLIB_CXX14LDFLAGS`

and ensuring these values are used in relevant compilations, after checking they are non-empty. A common use of src/Makefile is to compile an executable, when likely something like (for example for C++14)

CXX14 = `"${R_HOME}/bin/R" CMD config CXX14`
CXX14STD = `"${R_HOME}/bin/R" CMD config CXX14STD`
CXX = ${CXX14} ${CXX14STD}
CXXFLAGS = `"${R_HOME}/bin/R" CMD config CXX14FLAGS`

suffices. On Unix (and on Windows from R 4.3.0) this can be simplified to

CXX = ${CXX14} ${CXX14STD}
CXXFLAGS = ${CXX14FLAGS}

On a Unix-alike C++ compilation defaulted to C++11 from R 3.6.0, to C++14 from R 4.1.0 and to C++17 from R 4.3.0. However, only ‘if available’, so platforms using very old OSes might have used the previous default. Even older versions of R defaulted to the compiler’s default, almost certainly C++98 for compilers of comparable vintage.

On Windows the default was changed from C++98 to C++11 in R 3.6.2, to C++14 in R 4.2.3 and to C++17 in R 4.3.0.

The C++11 standard could be specified as from R 3.1.0 and C++14 or C++17 as from R 3.4.0, for C++20 from R 4.0.0 and for C++23 from R 4.3.0 (although they may not be supported by the compilers in use). C++11 support became mandatory in R 4.0.0 and C++17 support in R 4.4.0.

The .so/.dll in a package may need to be linked by the C++ compiler if it or any library it links to contains compiled C++ code. Dynamic linking usually brings in the C++ runtime library (commonly libstdc++ but can be, for example, libc++) but static linking (as used for external libraries on Windows and macOS) will not. R CMD INSTALL will link with the C++ compiler if there are any top-level C++ files in src, but not if these are all in subdirectories. The simplest way to force linking by the C++ compiler is to include an empty C++ file in src..

1.2.8 C standards

C has had standards C89/C90, C99, C11, C17 (also known as C18), and C23 is in final draft and expected to be published in 2024. C11 was a minor change to C99 which introduced some new features and made others optional, and C17 is a ‘bug-fix’ update to C11. On the other hand, C23 makes extensive changes, including making bool, true and false reserved words, finally disallowing K&R-style function declarations and clarifying the formerly deprecated meaning of function declarations with an empty parameter list to mean zero parameters. (There are many other additions: see for example https://en.cppreference.com/w/c/23.)

The configure script in recent versions of R aims to choose a C compiler which supports C11: as the default in recent versions of gcc, LLVM clang and Apple clang is C17, that is what is likely to be chosen. On the other hand, until R 4.3.0 the makefiles for the Windows build specified C99. They now use the compiler default which for the recommended compiler is C17.

Packages may want to either avoid or embrace the changes in C23, and can do so via specifying USE_Cnn for 17, 23, 90 or 99 in the SystemRequirements field of their DESCRIPTION file of a package depending on R (>= 4.3.0). Those using a configure script should set the corresponding compiler and flags, for example using

CC=`"${R_HOME}/bin/R" CMD config CC23`
CFLAGS=`"${R_HOME}/bin/R" CMD config C23FLAGS`
CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`
LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS`

The (claimed) C standard in use can be checked by the macro __STDC_VERSION__. This is undefined in C89/C90 and should have values 199901L, 201112L and 201710L for C99, C11 and C17. As C23 is not yet published there is as yet no definitive value: compilers are currently using 202000L. C23 has macros similar to C++ ‘feature tests’ for many of its changes, for example __STDC_VERSION_LIMITS_H__.

However, note the ‘claimed’ as no compiler had 100% conformance, and it is better to use configure to test for the feature you want to use than to condition on the value of __STDC_VERSION__. In particular, C11 alignment functionality such as _Alignas and aligned_alloc is not implemented on Windows.

End users can specify a standard by something like R CMD INSTALL --use-C17. This overrides the SystemRequirements field, but not for any configure file.

1.2.9 Using cmake

Packages often wish to include the sources of other software and compile that for inclusion in their .so or .dll, which is normally done by including (or unpacking) the sources in a subdirectory of src, as considered above.

Further issues arise when the external software uses another build system such as cmake, principally to ensure that all the settings for compilers, include and load paths etc are made. This section has already mentioned the need to set at least some of

CC CFLAGS CXX CXXFLAGS CPPFLAGS LDFLAGS

CFLAGS and CXXFLAGS will need to include CPICFLAGS and CXXPICFLAGS respectively unless (as below) cmake is asked to generate PIC code.

Setting these (and more) as environment variables controls the behaviour of cmake (https://cmake.org/cmake/help/latest/manual/cmake-env-variables.7.html#manual:cmake-env-variables(7)), but it may be desirable to translate these into native settings such as

CMAKE_C_COMPILER
CMAKE_C_FLAGS
CMAKE_CXX_COMPILER
CMAKE_CXX_FLAGS
CMAKE_INCLUDE_PATH
CMAKE_LIBRARY_PATH
CMAKE_SHARED_LINKER_FLAGS_INIT
CMAKE_OSX_DEPLOYMENT_TARGET

and it is often necessary to ensure a static library of PIC code is built by

-DBUILD_SHARED_LIBS:bool=OFF
-DCMAKE_POSITION_INDEPENDENT_CODE:bool=ON

If R is to be detected or used, this must be the build being used for package installation – "${R_HOME}"/bin/R.

To fix ideas, consider a package with sources for a library myLib under src/libs. Two approaches have been used. It is often most convenient to build the external software in a directory other than its sources (particularly during development when the build directory can be removed between builds rather than attempting to clean the sources) – this is illustrated in the first approach.

1. Use the package’s configure script to create a static library src/build/libmyLib.a. This can then be treated in the same way as external software, for example having in src/Makevars

   PKG_CPPFLAGS = -Ilibs/include
   PKG_LIBS = build/libmyLib.a

   (-Lbuild -lmyLib could also be used but this explicit specification avoids any confusion with dynamic libraries of the same name.)

   The configure script will need to contain something like (for C code)

   : ${R_HOME=`R RHOME`}
   if test -z "${R_HOME}"; then
     echo "could not determine R_HOME"
     exit 1
   fi
   CC=`"${R_HOME}/bin/R" CMD config CC`
   CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS`
   CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`
   LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS`
   
   cd src
   mkdir build && cd build
   cmake -S ../libs \
     -DCMAKE_BUILD_TYPE=Release \
     -DBUILD_SHARED_LIBS:bool=OFF \
     -DCMAKE_POSITION_INDEPENDENT_CODE:bool=ON
   ${MAKE}

2. Use src/Makevars (or src/Makevars.win or Makevars.ucrt) to build within the subdirectory. This could be something like (for C code)

   PKG_CPPFLAGS = -Ilibs/include
   PKG_LIBS = libs/libmyLib.a
   
   $(SHLIB): mylibs
   
   mylibs:
           (cd libs; \
             CC="$(CC)" CFLAGS="$(CFLAGS)" \
             CPPFLAGS="$(CPPFLAGS)" LDFLAGS="$(LDFLAGS)" \
             cmake . \
               -DCMAKE_BUILD_TYPE=Release \
               -DBUILD_SHARED_LIBS:bool=OFF \
               -DCMAKE_POSITION_INDEPENDENT_CODE:bool=ON; \
             $(MAKE))

   the compiler and other settings having been set as Make variables by an R makefile included by INSTALL before src/Makevars.

A complication is that on macOS cmake (where installed) is commonly not on the path but at /Applications/CMake.app/Contents/bin/cmake. One way to work around this is for the package’s configure script to include

if test -z "$CMAKE"; then CMAKE="`which cmake`"; fi
if test -z "$CMAKE"; then CMAKE=/Applications/CMake.app/Contents/bin/cmake; fi
if test -f "$CMAKE"; then echo "no 'cmake' command found"; exit 1; fi

and for the second approach to substitute CMAKE into src/Makevars. This also applies to the ancillary command ctest, if used.

1.3 Checking and building packages

Before using these tools, please check that your package can be installed. R CMD check will inter alia do this, but you may get more detailed error messages doing the install directly.

If your package specifies an encoding in its DESCRIPTION file, you should run these tools in a locale which makes use of that encoding: they may not work at all or may work incorrectly in other locales (although UTF-8 locales will most likely work).

Note: R CMD check and R CMD build run R processes with --vanilla in which none of the user’s startup files are read. If you need R_LIBS set (to find packages in a non-standard library) you can set it in the environment: also you can use the check and build environment files (as specified by the environment variables R_CHECK_ENVIRON and R_BUILD_ENVIRON; if unset, files⁵³ ~/.R/check.Renviron and ~/.R/build.Renviron are used) to set environment variables when using these utilities.

⁵³ On systems which use sub-architectures, architecture-specific versions such as ~/.R/check.Renviron.x64 take precedence.

Note to Windows users: R CMD build may make use of the Windows toolset (see The Windows toolset in R Installation and Administration) if present and in your path, and it is required for packages which need it to install (including those with configure.win, cleanup.win, configure.ucrt or cleanup.ucrt scripts or a src directory) and e.g. need vignettes built.

You may need to set the environment variable TMPDIR to point to a suitable writable directory with a path not containing spaces – use forward slashes for the separators. Also, the directory needs to be on a case-honouring file system (some network-mounted file systems are not).

1.3.1 Checking packages

Using R CMD check, the R package checker, one can test whether source R packages work correctly. It can be run on one or more directories, or compressed package tar archives with extension .tar.gz, .tgz, .tar.bz2 or .tar.xz.

It is strongly recommended that the final checks are run on a tar archive prepared by R CMD build.

This runs a series of checks, including

1. The package is installed. This will warn about missing cross-references and duplicate aliases in help files.

2. The file names are checked to be valid across file systems and supported operating system platforms.

3. The files and directories are checked for sufficient permissions (Unix-alikes only).

4. The files are checked for binary executables, using a suitable version of file if available⁵⁴. (There may be rare false positives.)

5. The DESCRIPTION file is checked for completeness, and some of its entries for correctness. Unless installation tests are skipped, checking is aborted if the package dependencies cannot be resolved at run time. (You may need to set R_LIBS in the environment if dependent packages are in a separate library tree.) One check is that the package name is not that of a standard package, nor one of the defunct standard packages (ctest, eda, lqs, mle, modreg, mva, nls, stepfun and ts). Another check is that all packages mentioned in library or requires or from which the NAMESPACE file imports or are called via :: or ::: are listed (in Depends, Imports, Suggests): this is not an exhaustive check of the actual imports.

6. Available index information (in particular, for demos and vignettes) is checked for completeness.

7. The package subdirectories are checked for suitable file names and for not being empty. The checks on file names are controlled by the option --check-subdirs=value. This defaults to default, which runs the checks only if checking a tarball: the default can be overridden by specifying the value as yes or no. Further, the check on the src directory is only run if the package does not contain a configure script (which corresponds to the value yes-maybe) and there is no src/Makefile or src/Makefile.in.

   To allow a configure script to generate suitable files, files ending in .in will be allowed in the R directory.

   A warning is given for directory names that look like R package check directories – many packages have been submitted to CRAN containing these.

8. The R files are checked for syntax errors. Bytes which are non-ASCII are reported as warnings, but these should be regarded as errors unless it is known that the package will always be used in the same locale.

9. It is checked that the package can be loaded, first with the usual default packages and then only with package base already loaded. It is checked that the namespace can be loaded in an empty session with only the base namespace loaded. (Namespaces and packages can be loaded very early in the session, before the default packages are available, so packages should work then.)

10. The R files are checked for correct calls to library.dynam. Package startup functions are checked for correct argument lists and (incorrect) calls to functions which modify the search path or inappropriately generate messages. The R code is checked for possible problems using codetools. In addition, it is checked whether S3 methods have all the arguments of the corresponding generic, and whether the final argument of replacement functions is called value. All foreign function calls (.C, .Fortran, .Call and .External calls) are tested to see if they have a PACKAGE argument, and if not, whether the appropriate DLL might be deduced from the namespace of the package. Any other calls are reported. (The check is generous, and users may want to supplement this by examining the output of tools::checkFF("mypkg", verbose=TRUE), especially if the intention were to always use a PACKAGE argument)

11. The Rd files are checked for correct syntax and metadata, including the presence of the mandatory fields (\name, \alias, \title and \description). The Rd name and title are checked for being non-empty, and there is a check for missing cross-references (links).

12. A check is made for missing documentation entries, such as undocumented user-level objects in the package.

13. Documentation for functions, data sets, and S4 classes is checked for consistency with the corresponding code.

14. It is checked whether all function arguments given in \usage sections of Rd files are documented in the corresponding \arguments section.

15. The data directory is checked for non-ASCII characters and for the use of reasonable levels of compression.

16. C, C++ and Fortran source and header files⁵⁵ are tested for portable (LF-only) line endings. If there is a Makefile or Makefile.in or Makevars or Makevars.in file under the src directory, it is checked for portable line endings and the correct use of $(BLAS_LIBS) and $(LAPACK_LIBS)

    Compiled code is checked for symbols corresponding to functions which might terminate R or write to stdout/stderr instead of the console. Note that the latter might give false positives in that the symbols might be pulled in with external libraries and could never be called. Windows⁵⁶ users should note that the Fortran and C++ runtime libraries are examples of such external libraries.

17. Some checks are made of the contents of the inst/doc directory. These always include checking for files that look like leftovers, and if suitable tools (such as qpdf) are available, checking that the PDF documentation is of minimal size.

18. The examples provided by the package’s documentation are run. (see Writing R documentation files, for information on using \examples to create executable example code.) If there is a file tests/Examples/pkg-Ex.Rout.save, the output of running the examples is compared to that file.

    Of course, released packages should be able to run at least their own examples. Each example is run in a ‘clean’ environment (so earlier examples cannot be assumed to have been run), and with the variables T and F redefined to generate an error unless they are set in the example: See Logical vectors in An Introduction to R.

19. If the package sources contain a tests directory then the tests specified in that directory are run. (Typically they will consist of a set of .R source files and target output files .Rout.save.) Please note that the comparison will be done in the end user’s locale, so the target output files should be ASCII if at all possible. (The command line option --test-dir=foo may be used to specify tests in a non-standard location. For example, unusually slow tests could be placed in inst/slowTests and then R CMD check --test-dir=inst/slowTests would be used to run them. Other names that have been suggested are, for example, inst/testWithOracle for tests that require Oracle to be installed, inst/randomTests for tests which use random values and may occasionally fail by chance, etc.)

20. The R code in package vignettes (see Writing package vignettes) is executed, and the vignettes re-made from their sources as a check of completeness of the sources (unless there is a BuildVignettes field in the package’s DESCRIPTION file with a false value). If there is a target output file .Rout.save in the vignette source directory, the output from running the code in that vignette is compared with the target output file and any differences are reported (but not recorded in the log file). (If the vignette sources are in the deprecated location inst/doc, do mark such target output files to not be installed in .Rinstignore.)

    If there is an error⁵⁷ in executing the R code in vignette foo.ext, a log file foo.ext.log is created in the check directory. The vignettes are re-made in a copy of the package sources in the vign_test subdirectory of the check directory, so for further information on errors look in directory pkgname/vign_test/vignettes. (It is only retained if there are errors or if environment variable _R_CHECK_CLEAN_VIGN_TEST_ is set to a false value.)

21. The PDF version of the package’s manual is created (to check that the Rd files can be converted successfully). This needs LaTeX and suitable fonts and LaTeX packages to be installed. See Making the manuals in R Installation and Administration for further details.

22. Optionally (including by R CMD check --as-cran) the HTML version of the manual is created and checked for compliance with the HTML5 standard. This requires a recent version⁵⁸ of ‘HTML Tidy’, either on the path or at a location specified by environment variable R_TIDYCMD. Up-to-date versions can be installed from http://binaries.html-tidy.org/.

⁵⁴ A suitable file.exe is part of the Windows toolset: it checks for gfile if a suitable file is not found: the latter is available in the OpenCSW collection for Solaris at https://www.opencsw.org/. The source repository is http://ftp.astron.com/pub/file/.

⁵⁵ An exception is made for subdirectories with names starting win or Win.

⁵⁶ on most other platforms such runtime libraries are dynamic, but static libraries are currently used on Windows because the toolchain is not a standard part of the OS.

⁵⁷ or if option --use-valgrind is used or environment variable _R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_ is set to a true value or if there are differences from a target output file

⁵⁸ for the most comprehensive checking this should be 5.8.0 or later: any for which tidy --version does not report a version number will be too old – this includes the 2006 version shipped with macOS.

All these tests are run with collation set to the C locale, and for the examples and tests with environment variable LANGUAGE=en: this is to minimize differences between platforms.

Use R CMD check --help to obtain more information about the usage of the R package checker. A subset of the checking steps can be selected by adding command-line options. It also allows customization by setting environment variables _R_CHECK_*_ as described in Tools in R Internals: a set of these customizations similar to those used by CRAN can be selected by the option --as-cran (which works best if Internet access is available). Some Windows users may need to set environment variable R_WIN_NO_JUNCTIONS to a non-empty value. The test of cyclic declarations⁵⁹in DESCRIPTION files needs repositories (including CRAN) set: do this in ~/.Rprofile, by e.g.

⁵⁹ For example, in early 2014 gdata declared Imports: gtools and gtools declared Imports: gdata.

options(repos = c(CRAN="https://cran.r-project.org"))

One check customization which can be revealing is

_R_CHECK_CODETOOLS_PROFILE_="suppressLocalUnused=FALSE"

which reports unused local assignments. Not only does this point out computations which are unnecessary because their results are unused, it also can uncover errors. (Two such are to intend to update an object by assigning a value but mistype its name or assign in the wrong scope, for example using <- where <<- was intended.) This can give false positives, most commonly because of non-standard evaluation for formulae and because the intention is to return objects in the environment of a function for later use.

Complete checking of a package which contains a file README.md needs a reasonably current version of pandoc installed: see https://pandoc.org/installing.html.

You do need to ensure that the package is checked in a suitable locale if it contains non-ASCII characters. Such packages are likely to fail some of the checks in a C locale, and R CMD check will warn if it spots the problem. You should be able to check any package in a UTF-8 locale (if one is available). Beware that although a C locale is rarely used at a console, it may be the default if logging in remotely or for batch jobs.

Often R CMD check will need to consult a CRAN repository to check details of uninstalled packages. Normally this defaults to the CRAN main site, but a mirror can be specified by setting environment variables R_CRAN_WEB and (rarely needed) R_CRAN_SRC to the URL of a CRAN mirror.

It is possible to install a package and then check the installed package. To do so first install the package and keep a log of the installation:

R CMD INSTALL -l libdir pkg > pkg.log 2>&1

and then use

Rdev CMD check -l libdir --install=check:pkg.log pkg

(Specifying the library is required: it ensures that the just-installed package is the one checked. If you know for sure only one copy is installed you can use --install=skip: this is used for R installation’s make check-recommended.)

1.3.2 Building package tarballs

Packages may be distributed in source form as “tarballs” (.tar.gz files) or in binary form. The source form can be installed on all platforms with suitable tools and is the usual form for Unix-like systems; the binary form is platform-specific, and is the more common distribution form for the macOS and x86_64 Windows platforms.

Using R CMD build, the R package builder, one can build R package tarballs from their sources (for example, for subsequent release). It is recommended that packages are built for release by the current release version of R or r-patched, to avoid inadvertently picking up new features of a development version of R.

Prior to actually building the package in the standard gzipped tar file format, a few diagnostic checks and cleanups are performed. In particular, it is tested whether object indices exist and can be assumed to be up-to-date, and C, C++ and Fortran source files and relevant makefiles in a src directory are tested and converted to LF line-endings if necessary.

Run-time checks whether the package works correctly should be performed using R CMD check prior to invoking the final build procedure.

To exclude files from being put into the package, one can specify a list of exclude patterns in file .Rbuildignore in the top-level source directory. These patterns should be Perl-like regular expressions (see the help for regexp in R for the precise details), one per line, to be matched case-insensitively against the file and directory names relative to the top-level package source directory. In addition, directories from source control systems⁶⁰ or from eclipse⁶¹, directories with names check, chm, or ending .Rcheck or Old or old and files GNUMakefile⁶², Read-and-delete-me or with base names starting with .#, or starting and ending with #, or ending in ~, .bak or .swp, are excluded by default⁶³. In addition, same-package tarballs (from previous builds) and their binary forms will be excluded from the top-level directory, as well as those files in the R, demo and man directories which are flagged by R CMD check as having invalid names.

⁶⁰ called CVS or .svn or .arch-ids or .bzr or .git (but not files called .git) or .hg.

⁶¹ called .metadata.

⁶² which is an error: GNU make uses GNUmakefile.

⁶³ see tools:::.hidden_file_exclusions and tools:::get_exclude_patterns() for further excluded files and file patterns, respectively.

Use R CMD build --help to obtain more information about the usage of the R package builder.

Unless R CMD build is invoked with the --no-build-vignettes option (or the package’s DESCRIPTION contains BuildVignettes: no or similar), it will attempt to (re)build the vignettes (see Writing package vignettes) in the package. To do so it installs the current package into a temporary library tree, but any dependent packages need to be installed in an available library tree (see the Note: at the top of this section).

Similarly, if the .Rd documentation files contain any \Sexpr macros (see Dynamic pages), the package will be temporarily installed to execute them. Post-execution binary copies of those pages containing build-time macros will be saved in build/partial.rdb. If there are any install-time or render-time macros, a .pdf version of the package manual will be built and installed in the build subdirectory. (This allows CRAN or other repositories to display the manual even if they are unable to install the package.) This can be suppressed by the option --no-manual or if package’s DESCRIPTION contains BuildManual: no or similar.

One of the checks that R CMD build runs is for empty source directories. These are in most (but not all) cases unintentional, if they are intentional use the option --keep-empty-dirs (or set the environment variable _R_BUILD_KEEP_EMPTY_DIRS_ to TRUE, or have a BuildKeepEmpty field with a true value in the DESCRIPTION file).

The --resave-data option allows saved images (.rda and .RData files) in the data directory to be optimized for size. It will also compress tabular files and convert .R files to saved images. It can take values no, gzip (the default if this option is not supplied, which can be changed by setting the environment variable _R_BUILD_RESAVE_DATA_) and best (equivalent to giving it without a value), which chooses the most effective compression. Using best adds a dependence on R (>= 2.10) to the DESCRIPTION file if bzip2 or xz compression is selected for any of the files. If this is thought undesirable, --resave-data=gzip (which is the default if that option is not supplied) will do what compression it can with gzip. A package can control how its data is resaved by supplying a BuildResaveData field (with one of the values given earlier in this paragraph) in its DESCRIPTION file.

The --compact-vignettes option will run tools::compactPDF over the PDF files in inst/doc (and its subdirectories) to losslessly compress them. This is not enabled by default (it can be selected by environment variable _R_BUILD_COMPACT_VIGNETTES_) and needs qpdf (https://qpdf.sourceforge.io/) to be available.

It can be useful to run R CMD check --check-subdirs=yes on the built tarball as a final check on the contents.

Where a non-POSIX file system is in use which does not utilize execute permissions, some care is needed with permissions. This applies on Windows and to e.g. FAT-formatted drives and SMB-mounted file systems on other OSes. The ‘mode’ of the file recorded in the tarball will be whatever file.info() returns. On Windows this will record only directories as having execute permission and on other OSes it is likely that all files have reported ‘mode’ 0777. A particular issue is packages being built on Windows which are intended to contain executable scripts such as configure and cleanup: R CMD build ensures those two are recorded with execute permission.

Directory build of the package sources is reserved for use by R CMD build: it contains information which may not easily be created when the package is installed, including index information on the vignettes and, rarely, information on the help pages and perhaps a copy of the PDF reference manual (see above).

1.3.3 Building binary packages

Binary packages are compressed copies of installed versions of packages. They contain compiled shared libraries rather than C, C++ or Fortran source code, and the R functions are included in their installed form. The format and filename are platform-specific; for example, a binary package for Windows is usually supplied as a .zip file, and for the macOS platform the default binary package file extension is .tgz.

The recommended method of building binary packages is to use

R CMD INSTALL --build pkg

where pkg is either the name of a source tarball (in the usual .tar.gz format) or the location of the directory of the package source to be built. This operates by first installing the package and then packing the installed binaries into the appropriate binary package file for the particular platform.

By default, R CMD INSTALL --build will attempt to install the package into the default library tree for the local installation of R. This has two implications:

• If the installation is successful, it will overwrite any existing installation of the same package.
• The default library tree must have write permission; if not, the package will not install and the binary will not be created.

To prevent changes to the present working installation or to provide an install location with write access, create a suitably located directory with write access and use the -l option to build the package in the chosen location. The usage is then

R CMD INSTALL -l location --build pkg

where location is the chosen directory with write access. The package will be installed as a subdirectory of location, and the package binary will be created in the current directory.

Other options for R CMD INSTALL can be found using R CMD INSTALL --help, and platform-specific details for special cases are discussed in the platform-specific FAQs.

Finally, at least one web-based service is available for building binary packages from (checked) source code: WinBuilder (see https://win-builder.R-project.org/) is able to build x86_64 Windows binaries. Note that this is intended for developers on other platforms who do not have access to Windows but wish to provide binaries for the Windows platform.

1.4 Writing package vignettes

In addition to the help files in Rd format, R packages allow the inclusion of documents in arbitrary other formats. The standard location for these is subdirectory inst/doc of a source package, the contents will be copied to subdirectory doc when the package is installed. Pointers from package help indices to the installed documents are automatically created. Documents in inst/doc can be in arbitrary format, however we strongly recommend providing them in PDF format, so users on almost all platforms can easily read them. To ensure that they can be accessed from a browser (as an HTML index is provided), the file names should start with an ASCII letter and be comprised entirely of ASCII letters or digits or hyphen or underscore.

A special case is package vignettes. Vignettes are documents in PDF or HTML format obtained from plain-text literate source files from which R knows how to extract R code and create output (in PDF/HTML or intermediate LaTeX). Vignette engines do this work, using “tangle” and “weave” functions respectively. Sweave, provided by the R distribution, is the default engine. Other vignette engines besides Sweave are supported; see Non-Sweave vignettes.

Package vignettes have their sources in subdirectory vignettes of the package sources. Note that the location of the vignette sources only affects R CMD build and R CMD check: the tarball built by R CMD build includes in inst/doc the components intended to be installed.

Sweave vignette sources are normally given the file extension .Rnw or .Rtex, but for historical reasons extensions⁶⁴ .Snw and .Stex are also recognized. Sweave allows the integration of LaTeX documents: see the Sweave help page in R and the Sweave vignette in package utils for details on the source document format.

⁶⁴ and to avoid problems with case-insensitive file systems, lower-case versions of all these extensions.

Package vignettes are tested by R CMD check by executing all R code chunks they contain (except those marked for non-evaluation, e.g., with option eval=FALSE for Sweave). The R working directory for all vignette tests in R CMD check is a copy of the vignette source directory. Make sure all files needed to run the R code in the vignette (data sets, …) are accessible by either placing them in the inst/doc hierarchy of the source package or by using calls to system.file(). All other files needed to re-make the vignettes (such as LaTeX style files, BibTeX input files and files for any figures not created by running the code in the vignette) must be in the vignette source directory. R CMD check will check that vignette production has succeeded by comparing modification times of output files in inst/doc with the source in vignettes.

R CMD build will automatically⁶⁵ create the (PDF or HTML versions of the) vignettes in inst/doc for distribution with the package sources. By including the vignette outputs in the package sources it is not necessary that these can be re-built at install time, i.e., the package author can use private R packages, screen snapshots and LaTeX extensions which are only available on their machine.⁶⁶

⁶⁵ unless inhibited by using BuildVignettes: no in the DESCRIPTION file.

⁶⁶ provided the conditions of the package’s license are met: many, including CRAN, see the omission of source components as incompatible with an Open Source license.

By default R CMD build will run Sweave on all Sweave vignette source files in vignettes. If Makefile is found in the vignette source directory, then R CMD build will try to run make after the Sweave runs, otherwise texi2pdf is run on each .tex file produced.

The first target in the Makefile should take care of both creation of PDF/HTML files and cleaning up afterwards (including after Sweave), i.e., delete all files that shall not appear in the final package archive. Note that if the make step runs R it needs to be careful to respect the environment values of R_LIBS and R_HOME⁶⁷. Finally, if there is a Makefile and it has a clean: target, make clean is run.

⁶⁷ R_HOME/bin is prepended to the PATH so that references to R or Rscript in the Makefile do make use of the currently running version of R.

All the usual caveats about including a Makefile apply. It must be portable (no GNU extensions), use LF line endings and must work correctly with a parallel make: too many authors have written things like

## BAD EXAMPLE
all: pdf clean

pdf: ABC-intro.pdf ABC-details.pdf

%.pdf:  %.tex
        texi2dvi --pdf $*

clean:
        rm *.tex ABC-details-*.pdf

which will start removing the source files whilst pdflatex is working.

Metadata lines can be placed in the source file, preferably in LaTeX comments in the preamble. One such is a \VignetteIndexEntry of the form

%\VignetteIndexEntry{Using Animal}

Others you may see are \VignettePackage (currently ignored), \VignetteDepends (a comma-separated list of package names) and \VignetteKeyword (which replaced \VignetteKeywords). These are processed at package installation time to create the saved data frame Meta/vignette.rds. The \VignetteEngine statement is described in Non-Sweave vignettes. Vignette metadata can be extracted from a source file using tools::vignetteInfo.

At install time an HTML index for all vignettes in the package is automatically created from the \VignetteIndexEntry statements unless a file index.html exists in directory inst/doc. This index is linked from the HTML help index for the package. If you do supply a inst/doc/index.html file it should contain relative links only to files under the installed doc directory, or perhaps (not really an index) to HTML help files or to the DESCRIPTION file, and be valid HTML as confirmed via the W3C Markup Validation Service or Validator.nu.

Sweave/Stangle allows the document to specify the split=TRUE option to create a single R file for each code chunk: this will not work for vignettes where it is assumed that each vignette source generates a single file with the vignette extension replaced by .R.

Do watch that PDFs are not too large – one in a CRAN package was 72MB! This is usually caused by the inclusion of overly detailed figures, which will not render well in PDF viewers. Sometimes it is much better to generate fairly high resolution bitmap (PNG, JPEG) figures and include those in the PDF document.

When R CMD build builds the vignettes, it copies these and the vignette sources from directory vignettes to inst/doc. To install any other files from the vignettes directory, include a file vignettes/.install_extras which specifies these as Perl-like regular expressions on one or more lines. (See the description of the .Rinstignore file for full details.)

1.4.1 Encodings and vignettes

Vignettes will in general include descriptive text, R input, R output and figures, LaTeX include files and bibliographic references. As any of these may contain non-ASCII characters, the handling of encodings can become very complicated.

The vignette source file should be written in ASCII or contain a declaration of the encoding (see below). This applies even to comments within the source file, since vignette engines process comments to look for options and metadata lines. When an engine’s weave and tangle functions are called on the vignette source, it will be converted to the encoding of the current R session.

Stangle() will produce an R code file in the current locale’s encoding: for a non-ASCII vignette what that is is recorded in a comment at the top of the file.

Sweave() will produce a .tex file in the current encoding, or in UTF-8 if that is declared. Non-ASCII encodings need to be declared to LaTeX via a line like

\usepackage[utf8]{inputenc}

(It is also possible to use the more recent inputenx LaTeX package.) For files where this line is not needed (e.g. chapters included within the body of a larger document, or non-Sweave vignettes), the encoding may be declared using a comment like

%\VignetteEncoding{UTF-8}

If the encoding is UTF-8, this can also be declared using the declaration

%\SweaveUTF8

If no declaration is given in the vignette, it will be assumed to be in the encoding declared for the package. If there is no encoding declared in either place, then it is an error to use non-ASCII characters in the vignette.

In any case, be aware that LaTeX may require the usepackage declaration.

Sweave() will also parse and evaluate the R code in each chunk. The R output will also be in the current locale (or UTF-8 if so declared), and should be covered by the inputenc declaration. One thing people often forget is that the R output may not be ASCII even for ASCII R sources, for many possible reasons. One common one is the use of ‘fancy’ quotes: see the R help on sQuote: note carefully that it is not portable to declare UTF-8 or CP1252 to cover such quotes, as their encoding will depend on the locale used to run Sweave(): this can be circumvented by setting options(useFancyQuotes="UTF-8") in the vignette.

The final issue is the encoding of figures – this applies only to PDF figures and not PNG etc. The PDF figures will contain declarations for their encoding, but the Sweave option pdf.encoding may need to be set appropriately: see the help for the pdf() graphics device.

As a real example of the complexities, consider the fortunes package version 1.4-0. That package did not have a declared encoding, and its vignette was in ASCII. However, the data it displays are read from a UTF-8 CSV file and will be assumed to be in the current encoding, so fortunes.tex will be in UTF-8 in any locale. Had read.table been told the data were UTF-8, fortunes.tex would have been in the locale’s encoding.

1.4.2 Non-Sweave vignettes

Vignettes in formats other than Sweave are supported via “vignette engines”. For example knitr version 1.1 or later can create .tex files from a variation on Sweave format, and .html files from a variation on “markdown” format. These engines replace the Sweave() function with other functions to convert vignette source files into LaTeX files for processing into .pdf, or directly into .pdf or .html files. The Stangle() function is replaced with a function that extracts the R source from a vignette.

R recognizes non-Sweave vignettes using filename extensions specified by the engine. For example, the knitr package supports the extension .Rmd (standing for “R markdown”). The user indicates the vignette engine within the vignette source using a \VignetteEngine line, for example

%\VignetteEngine{knitr::knitr}

This specifies the name of a package and an engine to use in place of Sweave in processing the vignette. As Sweave is the only engine supplied with the R distribution, the package providing any other engine must be specified in the VignetteBuilder field of the package DESCRIPTION file, and also specified in the Suggests, Imports or Depends field (since its namespace must be available to build or check your package). If more than one package is specified as a builder, they will be searched in the order given there. The utils package is always implicitly appended to the list of builder packages, but may be included earlier to change the search order.

Note that a package with non-Sweave vignettes should always have a VignetteBuilder field in the DESCRIPTION file, since this is how R CMD check recognizes that there are vignettes to be checked: packages listed there are required when the package is checked.

The vignette engine can produce .tex, .pdf, or .html files as output. If it produces .tex files, R will call texi2pdf to convert them to .pdf for display to the user (unless there is a Makefile in the vignettes directory).

Package writers who would like to supply vignette engines need to register those engines in the package .onLoad function. For example, that function could make the call

tools::vignetteEngine("knitr", weave = vweave, tangle = vtangle,
                      pattern = "[.]Rmd$", package = "knitr")

(The actual registration in knitr is more complicated, because it supports other input formats.) See the ?tools::vignetteEngine help topic for details on engine registration.

1.5 Package namespaces

R has a namespace management system for code in packages. This system allows the package writer to specify which variables in the package should be exported to make them available to package users, and which variables should be imported from other packages.

The namespace for a package is specified by the NAMESPACE file in the top level package directory. This file contains namespace directives describing the imports and exports of the namespace. Additional directives register any shared objects to be loaded and any S3-style methods that are provided. Note that although the file looks like R code (and often has R-style comments) it is not processed as R code. Only very simple conditional processing of if statements is implemented.

Packages are loaded and attached to the search path by calling library or require. Only the exported variables are placed in the attached frame. Loading a package that imports variables from other packages will cause these other packages to be loaded as well (unless they have already been loaded), but they will not be placed on the search path by these implicit loads. Thus code in the package can only depend on objects in its own namespace and its imports (including the base namespace) being visible⁶⁸.

⁶⁸ Note that lazy-loaded datasets are not in the package’s namespace so need to be accessed via ::, e.g. survival::survexp.us.

Namespaces are sealed once they are loaded. Sealing means that imports and exports cannot be changed and that internal variable bindings cannot be changed. Sealing allows a simpler implementation strategy for the namespace mechanism and allows code analysis and compilation tools to accurately identify the definition corresponding to a global variable reference in a function body.

The namespace controls the search strategy for variables used by functions in the package. If not found locally, R searches the package namespace first, then the imports, then the base namespace and then the normal search path (so the base namespace precedes the normal search rather than being at the end of it).

1.5.1 Specifying imports and exports

Exports are specified using the export directive in the NAMESPACE file. A directive of the form

export(f, g)

specifies that the variables f and g are to be exported. (Note that variable names may be quoted, and reserved words and non-standard names such as [<-.fractions must be.)

For packages with many variables to export it may be more convenient to specify the names to export with a regular expression using exportPattern. The directive

exportPattern("^[^.]")

exports all variables that do not start with a period. However, such broad patterns are not recommended for production code: it is better to list all exports or use narrowly-defined groups. (This pattern applies to S4 classes.) Beware of patterns which include names starting with a period: some of these are internal-only variables and should never be exported, e.g. .__S3MethodsTable__. (and loading excludes known cases).

Packages implicitly import the base namespace. Variables exported from other packages with namespaces need to be imported explicitly using the directives import and importFrom. The import directive imports all exported variables from the specified package(s). Thus the directives

import(foo, bar)

specifies that all exported variables in the packages foo and bar are to be imported. If only some of the exported variables from a package are needed, then they can be imported using importFrom. The directive

importFrom(foo, f, g)

specifies that the exported variables f and g of the package foo are to be imported. Using importFrom selectively rather than import is good practice and recommended notably when importing from packages with more than a dozen exports and especially from those written by others (so what they export can change in future).

To import every symbol from a package but for a few exceptions, pass the except argument to import. The directive

import(foo, except=c(bar, baz))

imports every symbol from foo except bar and baz. The value of except should evaluate to something coercible to a character vector, after substituting each symbol for its corresponding string.

It is possible to export variables from a namespace which it has imported from other namespaces: this has to be done explicitly and not via exportPattern.

If a package only needs a few objects from another package it can use a fully qualified variable reference in the code instead of a formal import. A fully-qualified reference to the function f in package foo is of the form foo::f. This is slightly less efficient than a formal import and also loses the advantage of recording all dependencies in the NAMESPACE file (but they still need to be recorded in the DESCRIPTION file). Evaluating foo::f will cause package foo to be loaded, but not attached, if it was not loaded already—this can be an advantage in delaying the loading of a rarely used package. However, if foo is listed only in Suggests or Enhances this also delays the check that it is installed: it is good practice to use such imports conditionally (e.g. via requireNamespace("foo", quietly = TRUE)).

Using the foo::f form will be necessary when a package needs to use a function of the same name from more than one namespace.

Using foo:::f instead of foo::f allows access to unexported objects. This is generally not recommended, as the existence or semantics of unexported objects may be changed by the package author in routine maintenance.

1.5.2 Registering S3 methods

The standard method for S3-style UseMethod dispatching might fail to locate methods defined in a package that is imported but not attached to the search path. To ensure that these methods are available the packages defining the methods should ensure that the generics are imported and register the methods using S3method directives. If a package defines a function print.foo intended to be used as a print method for class foo, then the directive

S3method(print, foo)

ensures that the method is registered and available for UseMethod dispatch, and the function print.foo does not need to be exported. Since the generic print is defined in base it does not need to be imported explicitly.

(Note that function and class names may be quoted, and reserved words and non-standard names such as [<- and function must be.)

It is possible to specify a third argument to S3method, the function to be used as the method, for example

S3method(print, check_so_symbols, .print.via.format)

when print.check_so_symbols is not needed.

As from R 3.6.0 one can also use S3method() directives to perform delayed registration. With

if(getRversion() >= "3.6.0") {
    S3method(pkg::gen, cls)
}

function gen.cls will get registered as an S3 method for class cls and generic gen from package pkg only when the namespace of pkg is loaded. This can be employed to deal with situations where the method is not “immediately” needed, and having to pre-load the namespace of pkg (and all its strong dependencies) in order to perform immediate registration is considered too onerous.

1.5.3 Load hooks

There are a number of hooks called as packages are loaded, attached, detached, and unloaded. See help(".onLoad") for more details.

Since loading and attaching are distinct operations, separate hooks are provided for each. These hook functions are called .onLoad and .onAttach. They both take arguments⁶⁹ libname and pkgname; they should be defined in the namespace but not exported.

⁶⁹ they will be called with two unnamed arguments, in that order.

Packages can use a .onDetach or .Last.lib function (provided the latter is exported from the namespace) when detach is called on the package. It is called with a single argument, the full path to the installed package. There is also a hook .onUnload which is called when the namespace is unloaded (via a call to unloadNamespace, perhaps called by detach(unload = TRUE)) with argument the full path to the installed package’s directory. Functions .onUnload and .onDetach should be defined in the namespace and not exported, but .Last.lib does need to be exported.

Packages are not likely to need .onAttach (except perhaps for a start-up banner); code to set options and load shared objects should be placed in a .onLoad function, or use made of the useDynLib directive described next.

User-level hooks are also available: see the help on function setHook.

These hooks are often used incorrectly. People forget to export .Last.lib. Compiled code should be loaded in .onLoad (or via a useDynLb directive: see below) and unloaded in .onUnload. Do remember that a package’s namespace can be loaded without the namespace being attached (e.g. by pkgname::fun) and that a package can be detached and re-attached whilst its namespace remains loaded.

It is good practice for these functions to be quiet. Any messages should use packageStartupMessage so users (include check scripts) can suppress them if desired.

1.5.4 useDynLib

A NAMESPACE file can contain one or more useDynLib directives which allows shared objects that need to be loaded.⁷⁰ The directive

⁷⁰ NB: this will only be read in all versions of R if the package contains R code in a R directory.

useDynLib(foo)

registers the shared object foo⁷¹ for loading with library.dynam. Loading of registered object(s) occurs after the package code has been loaded and before running the load hook function. Packages that would only need a load hook function to load a shared object can use the useDynLib directive instead.

⁷¹ Note that this is the basename of the shared object, and the appropriate extension (.so or .dll) will be added.

The useDynLib directive also accepts the names of the native routines that are to be used in R via the .C, .Call, .Fortran and .External interface functions. These are given as additional arguments to the directive, for example,

useDynLib(foo, myRoutine, myOtherRoutine)

By specifying these names in the useDynLib directive, the native symbols are resolved when the package is loaded and R variables identifying these symbols are added to the package’s namespace with these names. These can be used in the .C, .Call, .Fortran and .External calls in place of the name of the routine and the PACKAGE argument. For instance, we can call the routine myRoutine from R with the code

 .Call(myRoutine, x, y)

rather than

 .Call("myRoutine", x, y, PACKAGE = "foo")

There are at least two benefits to this approach. Firstly, the symbol lookup is done just once for each symbol rather than each time the routine is invoked. Secondly, this removes any ambiguity in resolving symbols that might be present in more than one DLL. However, this approach is nowadays deprecated in favour of supplying registration information (see below).

In some circumstances, there will already be an R variable in the package with the same name as a native symbol. For example, we may have an R function in the package named myRoutine. In this case, it is necessary to map the native symbol to a different R variable name. This can be done in the useDynLib directive by using named arguments. For instance, to map the native symbol name myRoutine to the R variable myRoutine_sym, we would use

useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)

We could then call that routine from R using the command

 .Call(myRoutine_sym, x, y)

Symbols without explicit names are assigned to the R variable with that name.

In some cases, it may be preferable not to create R variables in the package’s namespace that identify the native routines. It may be too costly to compute these for many routines when the package is loaded if many of these routines are not likely to be used. In this case, one can still perform the symbol resolution correctly using the DLL, but do this each time the routine is called. Given a reference to the DLL as an R variable, say dll, we can call the routine myRoutine using the expression

 .Call(dll$myRoutine, x, y)

The $ operator resolves the routine with the given name in the DLL using a call to getNativeSymbol. This is the same computation as above where we resolve the symbol when the package is loaded. The only difference is that this is done each time in the case of dll$myRoutine.

In order to use this dynamic approach (e.g., dll$myRoutine), one needs the reference to the DLL as an R variable in the package. The DLL can be assigned to a variable by using the variable = dllName format used above for mapping symbols to R variables. For example, if we wanted to assign the DLL reference for the DLL foo in the example above to the variable myDLL, we would use the following directive in the NAMESPACE file:

myDLL = useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)

Then, the R variable myDLL is in the package’s namespace and available for calls such as myDLL$dynRoutine to access routines that are not explicitly resolved at load time.

If the package has registration information (see Registering native routines), then we can use that directly rather than specifying the list of symbols again in the useDynLib directive in the NAMESPACE file. Each routine in the registration information is specified by giving a name by which the routine is to be specified along with the address of the routine and any information about the number and type of the parameters. Using the .registration argument of useDynLib, we can instruct the namespace mechanism to create R variables for these symbols. For example, suppose we have the following registration information for a DLL named myDLL:

static R_NativePrimitiveArgType foo_t[] = {
    REALSXP, INTSXP, STRSXP, LGLSXP
};

static const R_CMethodDef cMethods[] = {
   {"foo", (DL_FUNC) &foo, 4, foo_t},
   {"bar_sym", (DL_FUNC) &bar, 0},
   {NULL, NULL, 0, NULL}
};

static const R_CallMethodDef callMethods[] = {
   {"R_call_sym", (DL_FUNC) &R_call, 4},
   {"R_version_sym", (DL_FUNC) &R_version, 0},
   {NULL, NULL, 0}
};

Then, the directive in the NAMESPACE file

useDynLib(myDLL, .registration = TRUE)

causes the DLL to be loaded and also for the R variables foo, bar_sym, R_call_sym and R_version_sym to be defined in the package’s namespace.

Note that the names for the R variables are taken from the entry in the registration information and do not need to be the same as the name of the native routine. This allows the creator of the registration information to map the native symbols to non-conflicting variable names in R, e.g. R_version to R_version_sym for use in an R function such as

R_version <- function()
{
  .Call(R_version_sym)
}

Using argument .fixes allows an automatic prefix to be added to the registered symbols, which can be useful when working with an existing package. For example, package KernSmooth has

useDynLib(KernSmooth, .registration = TRUE, .fixes = "F_")

which makes the R variables corresponding to the Fortran symbols F_bkde and so on, and so avoid clashes with R code in the namespace.

NB: Using these arguments for a package which does not register native symbols merely slows down the package loading (although many CRAN packages have done so). Once symbols are registered, check that the corresponding R variables are not accidentally exported by a pattern in the NAMESPACE file.

1.5.5 An example

As an example consider two packages named foo and bar. The R code for package foo in file foo.R is

x <- 1 f <- function(y) c(x,y) foo <- function(x) .Call("foo", x, PACKAGE="foo") print.foo <- function(x, ...) cat("<a foo>\n")

Some C code defines a C function compiled into DLL foo (with an appropriate extension). The NAMESPACE file for this package is

useDynLib(foo) export(f, foo) S3method(print, foo)

The second package bar has code file bar.R

c <- function(...) sum(...) g <- function(y) f(c(y, 7)) h <- function(y) y+9

and NAMESPACE file

import(foo) export(g, h)

Calling library(bar) loads bar and attaches its exports to the search path. Package foo is also loaded but not attached to the search path. A call to g produces

> g(6)
[1]  1 13

This is consistent with the definitions of c in the two settings: in bar the function c is defined to be equivalent to sum, but in foo the variable c refers to the standard function c in base.

1.5.6 Namespaces with S4 classes and methods

Some additional steps are needed for packages which make use of formal (S4-style) classes and methods (unless these are purely used internally). The package should have Depends: methods⁷² in its DESCRIPTION and import(methods) or importFrom(methods, ...) plus any classes and methods which are to be exported need to be declared in the NAMESPACE file. For example, the stats4 package has

⁷² Imports: methods may suffice, but package code is little exercised without the methods package on the search path and may not be fully robust to this scenario.

export(mle) # exporting methods implicitly exports the generic
importFrom("stats", approx, optim, pchisq, predict, qchisq, qnorm, spline)
## For these, we define methods or (AIC, BIC, nobs) an implicit generic:
importFrom("stats", AIC, BIC, coef, confint, logLik, nobs, profile,
           update, vcov)
exportClasses(mle, profile.mle, summary.mle)
## All methods for imported generics:
exportMethods(coef, confint, logLik, plot, profile, summary,
              show, update, vcov)
## implicit generics which do not have any methods here
export(AIC, BIC, nobs)

All S4 classes to be used outside the package need to be listed in an exportClasses directive. Alternatively, they can be specified using exportClassPattern⁷³ in the same style as for exportPattern. To export methods for generics from other packages an exportMethods directive can be used.

⁷³ This defaults to the same pattern as exportPattern: use something like exportClassPattern("^$") to override this.

Note that exporting methods on a generic in the namespace will also export the generic, and exporting a generic in the namespace will also export its methods. If the generic function is not local to this package, either because it was imported as a generic function or because the non-generic version has been made generic solely to add S4 methods to it (as for functions such as coef in the example above), it can be declared via either or both of export or exportMethods, but the latter is clearer (and is used in the stats4 example above). In particular, for primitive functions there is no generic function, so export would export the primitive, which makes no sense. On the other hand, if the generic is local to this package, it is more natural to export the function itself using export(), and this must be done if an implicit generic is created without setting any methods for it (as is the case for AIC in stats4).

A non-local generic function is only exported to ensure that calls to the function will dispatch the methods from this package (and that is not done or required when the methods are for primitive functions). For this reason, you do not need to document such implicitly created generic functions, and undoc in package tools will not report them.

If a package uses S4 classes and methods exported from another package, but does not import the entire namespace of the other package⁷⁴, it needs to import the classes and methods explicitly, with directives

⁷⁴ if it does, there will be opaque warnings about replacing imports if the classes/methods are also imported.

importClassesFrom(package, ...)
importMethodsFrom(package, ...)

listing the classes and functions with methods respectively. Suppose we had two small packages A and B with B using A. Then they could have NAMESPACE files

export(f1, ng1) exportMethods("[") exportClasses(c1)

and

importFrom(A, ng1) importClassesFrom(A, c1) importMethodsFrom(A, f1) export(f4, f5) exportMethods(f6, "[") exportClasses(c1, c2)

respectively.

Note that importMethodsFrom will also import any generics defined in the namespace on those methods.

It is important if you export S4 methods that the corresponding generics are available. You may for example need to import coef from stats to make visible a function to be converted into its implicit generic. But it is better practice to make use of the generics exported by stats4 as this enables multiple packages to unambiguously set methods on those generics.

1.6 Writing portable packages

This section contains advice on writing packages to be used on multiple platforms or for distribution (for example to be submitted to a package repository such as CRAN).

Portable packages should have simple file names: use only alphanumeric ASCII characters and period (.), and avoid those names not allowed under Windows (see Package structure).

Many of the graphics devices are platform-specific: even X11() (aka x11()) which although emulated on Windows may not be available on a Unix-alike (and is not the preferred screen device on OS X). It is rarely necessary for package code or examples to open a new device, but if essential,⁷⁵ use dev.new().

⁷⁵ People use dev.new() to open a device at a particular size: that is not portable but using dev.new(noRStudioGD = TRUE) helps.

Use R CMD build to make the release .tar.gz file.

R CMD check provides a basic set of checks, but often further problems emerge when people try to install and use packages submitted to CRAN – many of these involve compiled code. Here are some further checks that you can do to make your package more portable.

• If your package has a configure script, provide a configure.win or configure.ucrt script to be used on Windows (an empty configure.win file if no actions are needed).

• If your package has a Makevars or Makefile file, make sure that you use only portable make features. Such files should be LF-terminated⁷⁶ (including the final line of the file) and not make use of GNU extensions. (The POSIX specification is available at https://pubs.opengroup.org/onlinepubs/9699919799/utilities/make.html; anything not documented there should be regarded as an extension to be avoided. Further advice can be found at https://www.gnu.org/software/autoconf/manual/autoconf.html#Portable-Make. ) Commonly misused GNU extensions are conditional inclusions (ifeq and the like), ${shell ...}, ${wildcard ...} and similar, and the use of +=⁷⁷ and :=. Also, the use of $< other than in implicit rules is a GNU extension, as is the $^ macro. As is the use of .PHONY (some other makes ignore it). Unfortunately makefiles which use GNU extensions often run on other platforms but do not have the intended results.

  Note that the -C flag for make is not included in the POSIX specification and is not implemented by some of the makes which have been used with R. However, it is more commonly implemented (e.g. by FreeBSD make) than the GNU-specific --directory=.

  You should not rely on built-in/default make rules, even when specified by POSIX, as some makes do not have the POSIX ones and others have altered them.

  The use of ${shell ...} can be avoided by using backticks, e.g.

  PKG_CPPFLAGS = `gsl-config --cflags`

  which works in all versions of make known⁷⁸ to be used with R.

  If you really must require GNU make, declare it in the DESCRIPTION file by

  SystemRequirements: GNU make

  and ensure that you use the value of environment variable MAKE (and not just make) in your scripts. (On some platforms GNU make is available under a name such as gmake, and there SystemRequirements is used to set MAKE.) Your configure script (or similar) does need to check that the executable pointed to by MAKE is indeed GNU make.

  If you only need GNU make for parts of the package which are rarely needed (for example to create bibliography files under vignettes), use a file called GNUmakefile rather than Makefile as GNU make (only) will use the former.

  macOS has used GNU make for many years (it previously used BSD make), but the version has been frozen at 3.81 (from 2006).

  Since the only viable make for Windows is GNU make, it is permissible to use GNU extensions in files Makevars.win, Makevars.ucrt, Makefile.win or Makefile.ucrt.

• If you use src/Makevars to compile code in a subdirectory, ensure that you have followed all the advice above. In particular

  • Anticipate a parallel make. See Using Makevars.
  • Pass macros down to the makefile in the subdirectory, including all the needed compiler flags (including PIC and visibility flags). If they are used (even by a default rule) in the subdirectory’s Makefile, this includes macros AR and RANLIB. See Compiling in sub-directories, which has a C example. A C++ example: makefile pkg/libpkg.a: (cd pkg && $(MAKE) -f make_pkg libpkg.a \ CXX="$(CXX)" CXXFLAGS="$(CXXFLAGS) $(CXXPICFLAGS) $(C_VISIBILITY)" \ AR="$(AR)" RANLIB="$(RANLIB)")
  • Ensure that cleanup will be performed by R CMD build, for example in a cleanup script or a clean target.

• If your package uses a src/Makefile file to compile code to be linked into R, ensure that it uses exactly the same compiler and flag settings that R uses when compiling such code: people often forget PIC flags. If R CMD config is used, this needs something like (for C++) makefile RBIN = `"${R_HOME}/bin/R"` CXX = `"${RBIN}" CMD config CXX` CXXFLAGS = `"${RBIN}" CMD config CXXFLAGS` `"${RBIN}" CMD config CXXPICFLAGS`

• Names of source files including = (such as src/complex_Sig=gen.c) will confuse some make programs and should be avoided.

• Bash extensions also need to be avoided in shell scripts, including expressions in Makefiles (which are passed to the shell for processing). Some R platforms use strict⁷⁹ Bourne shells: an earlier R toolset on Windows⁸⁰ and some Unix-alike OSes use ash (https://en.wikipedia.org/wiki/Almquist_shell, a ’lightweight shell with few builtins) or derivatives such as dash. Beware of assuming that all the POSIX command-line utilities are available, especially on Windows where only a subset (which has changed by version of Rtools) is provided for use with R. One particular issue is the use of echo, for which two behaviours are allowed (https://pubs.opengroup.org/onlinepubs/9699919799/utilities/echo.html) and both have occurred as defaults on R platforms: portable applications should use neither -n (as the first argument) nor escape sequences. The recommended replacement for echo -n is the command printf. Another common issue is the construction

  export FOO=value

  which is bash-specific (first set the variable then export it by name).

  Using test -e (or [ -e ]) in shell scripts is not fully portable⁸¹: -f is normally what is intended. Flags -a and -o are nowadays declared obsolescent by POSIX and should not be used.

  Use of ‘brace expansion’, e.g.,

  rm -f src/*.{o,so,d}

  is not portable.

  The -o flag for set in shell scripts is optional in POSIX and not supported on all the platforms R is used on.

  The variable OSTYPE is shell-specific and its values are rather unpredictable and may include a version such as darwin19.0: uname is often what is intended (with common values Darwin, Linux and SunOS).

  On macOS which shell /bin/sh invokes is user- and platform-dependent: it might be bash version 3.2, dash or zsh (for new accounts it is zsh, for accounts ported from Mojave or earlier it is usually bash).

• R is not built by default as a shared library on non-Windows platforms (although it commonly is on macOS to support the GUI), so there need not be a file libR.so nor libR.dylib. Users of cmake or rust have all too frequently assumed otherwise, so do ensure your package is checked under a vanilla R build. See Configuration options in R Installation and Administration for more information.

• Make use of the abilities of your compilers to check the standards-conformance of your code. For example, gcc, clang and gfortran⁸² can be used with options -Wall -pedantic to alert you to potential problems. This is particularly important for C++, where g++ -Wall -pedantic will alert you to the use of some of the GNU extensions which fail to compile on most other C++ compilers. If R was not configured accordingly, one can achieve this via personal Makevars files. See Customizing package compilation in R Installation and Administration for more information.

  Portable C++ code needs to follow all of the 2011, 2014 and 2017 standards (including not using deprecated/removed features) or to specify C+11/14/17/20/23 where available (which is not the case on all R platforms). Currently C++20 support is patchy across R platforms.

  If using Fortran with the GNU compiler, use the flags -std=f95 -Wall -pedantic which reject most GNU extensions and features from later standards. (Although R only requires Fortran 90, gfortran does not have a way to specify that standard.) Also consider -std=f2008 as some recent compilers have Fortran 2008 or even 2018 as the minimum supported standard.

  As from macOS 11 (late 2020), its C compiler sets the flag -Werror=implicit-function-declaration by default which forces stricter conformance to C99. This can be used on other platforms with gcc or clang. If your package has a (autoconf-generated) configure script, try installing it whilst using this flag, and read through the config.log file — compilation warnings and errors can lead to features which are present not being detected. (If possible do this on several platforms.)

• R CMD check performs some checks for non-portable compiler/linker flags in src/Makevars. However, it cannot check the meaning of such flags, and some are commonly accepted but with compiler-specific meanings. There are other non-portable flags which are not checked, nor are src/Makefile files and makefiles in sub-directories. As a comment in the code says

  It is hard to think of anything apart from -I* and -D* that is safe for general use …

  although -pthread is pretty close to portable. (Option -U is portable but little use on the command line as it will only cancel built-in defines (not portable) and those defined earlier on the command line (R does not use any).)

  The GNU option -pipe used to be widely accepted by C/C++/Fortran compilers, but has been removed in flang-new 18. In any case, it should not be used in distributed code as it may lead to excessive memory use.

  People have used configure to customize src/Makevars, including for specific compilers. This is unsafe for several reasons. First, unintended compilers might meet the check—for example, several compilers other than GCC identify themselves as ‘GCC’ whilst being only partially conformant. Second, future versions of compilers may behave differently (including updates to quite old series) so for example -Werror (and specializations) can make a package non-installable under a future version. Third, using flags to suppress diagnostic messages can hide important information for debugging on a platform not tested by the package maintainer. (R CMD check can optionally report on unsafe flags which were used.)

  Avoid the use of -march and especially -march=native. This allows the compiler to generate code that will only run on a particular class of CPUs (that of the compiling machine for native). People assume this is a ‘minimum’ CPU specification, but that is not how it is documented for gcc (it is accepted by clang but apparently it is undocumented what precisely it does, and it can be accepted and may be ignored for other compilers). (For personal use -mtune is safer, but still not portable enough to be used in a public package.) Not even gcc supports native for all CPUs, and it can do surprising things if it finds a CPU released later than its version.

• Do be very careful with passing arguments between R, C and Fortran code. In particular, long in C will be 32-bit on some R platforms (including 64-bit Windows), but 64-bit on most modern Unix and Linux platforms. It is rather unlikely that the use of long in C code has been thought through: if you need a longer type than int you should use a configure test for a C99/C++11 type such as int_fast64_t (and failing that, long long) and typedef your own type, or use another suitable type (such as size_t, but beware that is unsigned and ssize_t is not portable).

  It is not safe to assume that long and pointer types are the same size, and they are not on 64-bit Windows. If you need to convert pointers to and from integers use the C99/C++11 integer types intptr_t and uintptr_t (in the headers <stdint.h> and <cstdint>: they are not required to be implemented by the standards but are used in C code by R itself).

  Note that integer in Fortran corresponds to int in C on all R platforms. There is no such guarantee for Fortran logical, and recent gfortran maps it to int_least32_t on most platforms.

• Under no circumstances should your compiled code ever call abort or exit⁸³: these terminate the user’s R process, quite possibly losing all unsaved work. One usage that could call abort is the assert macro in C or C++ functions, which should never be active in production code. The normal way to ensure that is to define the macro NDEBUG, and R CMD INSTALL does so as part of the compilation flags. Beware of including headers (including from other packages) which could undefine it, now or in future versions. If you wish to use assert during development. you can include -UNDEBUG in PKG_CPPFLAGS or #undef it in your headers or code files. Note that your own src/Makefile or makefiles in sub-directories may also need to define NDEBUG.

  This applies not only to your own code but to any external software you compile in or link to.

  Nor should Fortran code call STOP nor EXIT (a GNU extension).

• Compiled code should not write to stdout or stderr and C++ and Fortran I/O should not be used. As with the previous item such calls may come from external software and may never be called, but package authors are often mistaken about that.

• Compiled code should not call the system random number generators such as rand, drand48 and random⁸⁴, but rather use the interfaces to R’s RNGs described in Random number generation. In particular, if more than one package initializes a system RNG (e.g. via srand), they will interfere with each other. This applies also to Fortran 90’s random_number and random_seed, and Fortran 2018’s random_init. And to GNU Fortran’s rand, irand and srand. Except for drand48, what PRNG these functions use is implementation-dependent.

  Nor should the C++11 random number library be used nor any other third-party random number generators such as those in GSL.

• Use of sprintf and vsprintf is regarded as a potential security risk and warned about on some platforms.⁸⁵ R CMD check reports if any calls are found.

• Errors in memory allocation and reading/writing outside arrays are very common causes of crashes (e.g., segfaults) on some machines. See Checking memory access for tools which can be used to look for this.

• Many platforms will allow unsatisfied entry points in compiled code, but will crash the application (here R) if they are ever used. Some (notably Windows) will not. Looking at the output of

  nm -pg mypkg.so

  and checking if any of the symbols marked U is unexpected is a good way to avoid this.

• Linkers have a lot of freedom in how to resolve entry points in dynamically-loaded code, so the results may differ by platform. One area that has caused grief is packages including copies of standard system software such as libz (especially those already linked into R). In the case in point, entry point gzgets was sometimes resolved against the old version compiled into the package, sometimes against the copy compiled into R and sometimes against the system dynamic library. The only safe solution is to rename the entry points in the copy in the package. We have even seen problems with entry point name myprintf, which is a system entry point⁸⁶ on some Linux systems.

  A related issue is the naming of libraries built as part of the package installation. macOS and Windows have case-insensitive file systems, so using

  -L. -lLZ4

  in PKG_LIBS will match liblz4. And -L. only appends to the list of searched locations, and liblz4 might be found in an earlier-searched location (and has been). The only safe way is to give an explicit path, for example

  ./libLZ4.a

• Conflicts between symbols in DLLs are handled in very platform-specific ways. Good ways to avoid trouble are to make as many symbols as possible static (check with nm -pg), and to use names which are clearly tied to your package (which also helps users if anything does go wrong). Note that symbol names starting with R_ are regarded as part of R’s namespace and should not be used in packages.

• It is good practice for DLLs to register their symbols (see Registering native routines), restrict visibility (see Controlling visibility) and not allow symbol search (see Registering native routines). It should be possible for a DLL to have only one visible symbol, R_init_pkgname, on suitable platforms⁸⁷, which would completely avoid symbol conflicts.

• It is not portable to call compiled code in R or other packages via .Internal, .C, .Fortran, .Call or .External, since such interfaces are subject to change without notice and will probably result in your code terminating the R process.

• Do not use (hard or symbolic) file links in your package sources. Where possible R CMD build will replace them by copies.

• If you do not yourself have a Windows system, consider submitting your source package to WinBuilder (https://win-builder.r-project.org/) before distribution. If you need to check on an M1 Mac, there is a check service at https://mac.r-project.org/macbuilder/submit.html.

• It is bad practice for package code to alter the search path using library, require or attach and this often does not work as intended. For alternatives, see Suggested packages and with().

• Examples can be run interactively via example as well as in batch mode when checking. So they should behave appropriately in both scenarios, conditioning by interactive() the parts which need an operator or observer. For instance, progress bars⁸⁸ are only appropriate in interactive use, as is displaying help pages or calling View() (see below).

• Be careful with the order of entries in macros such as PKG_LIBS. Some linkers will re-order the entries, and behaviour can differ between dynamic and static libraries. Generally -L options should precede⁸⁹ the libraries (typically specified by -l options) to be found from those directories, and libraries are searched once in the order they are specified. Not all linkers allow a space after -L .

• Care is needed with the use of LinkingTo. This puts one or more directories on the include search path ahead of system headers but (prior to R 3.4.0) after those specified in the CPPFLAGS macro of the R build (which normally includes -I/usr/local/include, but most platforms ignore that and include it with the system headers).

  Any confusion would be avoided by having LinkingTo headers in a directory named after the package. In any case, name conflicts of headers and directories under package include directories should be avoided, both between packages and between a package and system and third-party software.

• The ar utility is often used in makefiles to make static libraries. Its modifier u is defined by POSIX but is disabled in GNU ar on some Linux distributions which use ‘deterministic mode’. The safest way to make a static library is to first remove any existing file of that name then use $(AR) -cr and then $(RANLIB) if needed (which is system-dependent: on most systems⁹⁰ ar always maintains a symbol table). The POSIX standard says options should be preceded by a hyphen (as in -cr), although most OSes accept them without. Note that on some systems ar -cr must have at least one file specified.

  The s modifier (to replace a separate call to ranlib) is required by X/OPEN but not POSIX, so ar -crs is not portable.

  For portability the AR and RANLIB macros should always be used – some builds require wrappers such as gcc-ar or extra arguments to specify plugins.

• The strip utility is platform-specific (and CRAN prohibits removing debug symbols). For example the options --strip-debug and --strip-unneeded of the GNU version are not supported on macOS: the POSIX standard for strip does not mention any options, and what calling it without options does is platform-dependent. Stripping a .so file could even prevent it being dynamically loaded into R on an untested platform.

  ld -S invokes strip --strip-debug for GNU ld (and similarly on macOS) but is not portable: in particular on Solaris it did something completely different and took an argument.

• Some people have a need to set a locale. Locale names are not portable, and e.g. fr_FR.utf8 is commonly used on Linux but not accepted on macOS. fr_FR.UTF-8 is more portable, being accepted on recent Linux, AIX, FreeBSD, macOS and Solaris (at least). However, some Linux distributions micro-package, so locales defined by glibc (including these examples) may not be installed.

• Avoid spaces in file names, not least as they can cause difficulties for external tools. An example was a package with a knitr vignette that used spaces in plot names: this caused some older versions of pandoc to fail with a baffling error message.

  Non-ASCII filenames can also cause problems (particularly in non-UTF-8 locales).

• Take care in naming LaTeX macros (also known as ‘commands’) in vignette sources: if these are also defined in a future version of one of the LaTeX packages used there will be a fatal error. One instance in 2021 was package hyperref newly defining \C, \F, \G, \U and \textapprox. If you are confident that your definitions will be the only ones relevant you can use \renewcommand but it is better to use names clearly associated with your package.

• Make sure that any version requirement for Java code is both declared in the SystemRequirements field⁹¹ and tested at runtime (not least as the Java installation when the package is installed might not be the same as when the package is run and will not be for binary packages).

  When specifying a minimum Java version please use the official version names, which are (confusingly)

  1.1 1.2 1.3 1.4 5.0 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

  and as from 2018 a year.month scheme such as 18.9 is also in use. Fortunately only the integer values are likely to be relevant. If at all possible, use one of the LTS versions (8, 11, 17, 21 …) as the minimum version. The preferred form of version specification is

  SystemRequirements: Java (>= 11)

  A suitable test for Java at least version 8 for packages using rJava would be something like

  .jinit()
  jv <- .jcall("java/lang/System", "S", "getProperty", "java.runtime.version")
  if(substr(jv, 1L, 2L) == "1.") {
    jvn <- as.numeric(paste0(strsplit(jv, "[.]")[[1L]][1:2], collapse = "."))
    if(jvn < 1.8) stop("Java >= 8 is needed for this package but not available")
  }

  Java 9 changed the format of this string (which used to be something like 1.8.0_292-b10); Java 11 gave jv as 11+28 whereas Java 11.0.11 gave 11.0.11+9. (https://openjdk.org:443/jeps/322 details the current scheme. Note that it is necessary to allow for pre-releases like 11-ea+22.)

  Note too that the compiler used to produce a jar can impose a minimum Java version, often resulting in an arcane message like

  java.lang.UnsupportedClassVersionError: ... Unsupported major.minor version 52.0

  (Where https://en.wikipedia.org/wiki/Java_class_file maps class-file version numbers to Java versions.) Compile with something like javac -target 11 to ensure this is avoided. Note this also applies to packages distributing (or even downloading) compiled Java code produced by others, so their requirements need to be checked (they are often not documented accurately) and accounted for. It should be possible to check the class-file version via command-line utility javap, if necessary after extracting the .class files from a .jar archive. For example,

  jar xvf some.jar
  javap -verbose path/to/some.class | grep major

  Some packages have stated a requirement on a particular JDK, but a package should only be requiring a JRE unless providing its own Java interface.

  Java 8 is still in widespread use (and may remain so because of licence changes and support on older OSes: OpenJDK has security support until March 2026). On the other hand, newer platforms may only have support for recent versions of Java: for arm64 macOS the first officially supported version was

• A package with a hard-to-satisfy system requirement is by definition not portable, annoyingly so if this is not declared in the SystemRequirements field. The most common example is the use of pandoc, which is only available for a very limited range of platforms (and has onerous requirements to install from source) and has capabilities⁹² that vary by build but are not documented. Several recent versions of pandoc for macOS did not work on R’s then target of High Sierra (and this too was undocumented). Another example is the Rust compilation system (cargo and rustc).

  Usage of external commands should always be conditional on a test for presence (perhaps using Sys.which), as well as declared in the SystemRequirements field. A package should pass its checks without warnings nor errors without the external command being present.

  An external command can be a (possibly optional) requirement for an imported or suggested package but needed for examples, tests or vignettes in the package itself. Such usages should always be declared and conditional.

  Interpreters for scripting languages such as Perl, Python and Ruby need to be declared as system requirements and used conditionally: for example macOS 10.16 was announced not to have them (but released as macOS 11 with them); later it was announced that macOS 12.3 does not have Python 2 and only a minimal install of Python 3 is included. Python 2 has passed end-of-life and been removed from many major distributions. Support for Rust or Go cannot be assumed.

  Command cmake is not commonly installed, and where it is, it might not be on the path. In particular, the most common location on macOS is /Applications/CMake.app/Contents/bin/cmake and that should be looked for if cmake is not found on the path.

• Be sure to use portable encoding names: none of utf8, mac and macroman is portable. See the help for file for more details.

• Do not invoke R by plain R, Rscript or (on Windows) Rterm in your examples, tests, vignettes, makefiles or other scripts. As pointed out in several places earlier in this manual, use something like

  "$(R_HOME)/bin/Rscript"
  "$(R_HOME)/bin$(R_ARCH_BIN)/Rterm"

  with appropriate quotes (as, although not recommended, R_HOME can contain spaces).

• Do not use R_HOME in makefiles except when passing them to the shell. Specifically, do not use R_HOME in the argument to include, as R_HOME can contain spaces. Quoting the argument to include does not help. A portable and the recommended way to avoid the problem of spaces in ${R_HOME} is using option -f of make. This is easy to do with recursive invocation of make, which is also the only usual situation when R_HOME is needed in the argument for include. makefile $(MAKE) -f "${R_HOME}/etc${R_ARCH}/Makeconf" -f Makefile.inner

• If distributing datasets involving date-times, consider if a time zone needs to be specified. The most portable way to distribute date-times is as objects of class "POSIXct" and as these record the time in UTC, the time represented is independent of the time zone: but how it is printed may not be. Objects of class "POSIXlt" should have a "tzone" attribute. Dates (e.g, birthdays) are conventionally considered independently of time zone.

• If at all possible avoid any Internet access during package installation. Installation and use may well be on different machines/accounts and those allowed to install software may have no Internet access, and being self-contained helps ensure long-term reproducibility.

⁷⁶ Solaris make did not accept CRLF-terminated Makefiles; Solaris warned about and some other makes ignore incomplete final lines.

⁷⁷ This was apparently introduced in SunOS 4, and is available elsewhere provided it is surrounded by spaces.

⁷⁸ GNU make, BSD make and other variants of pmake in FreeBSD, NetBSD and formerly in macOS, and formerly AT&T make as implemented on Solaris and ‘Distributed Make’ (dmake), part of Oracle Developer Studio and available in other versions including from Apache OpenOffice.

⁷⁹ For example, test options -a and -e are not portable, and not supported in the AT&T Bourne shell used on Solaris 10/11, even though they are in the POSIX standard. Nor did Solaris support $(cmd).

⁸⁰ as from R 4.0.0 the default is bash.

⁸¹ it was not in the Bourne shell, and was not supported by Solaris 10.

⁸² https://fortranwiki.org/fortran/show/Modernizing+Old+Fortran may help explain some of the warnings from gfortran -Wall -pedantic.

⁸³ or where supported the variants _Exit and _exit.

⁸⁴ This and srandom are in any case not portable. They are in POSIX but not in the C99 standard, and not available on Windows.

⁸⁵ including macOS as from version 13.

⁸⁶ in libselinux.

⁸⁷ At least Linux and Windows, but not macOS.

⁸⁸ except perhaps the simplest kind as used by download.file() in non-interactive use.

⁸⁹ Whereas the GNU linker reorders so -L options are processed first, the Solaris one did not.

⁹⁰ some versions of macOS did not.

⁹¹ If a Java interpreter is required directly (not via rJava) this must be declared and its presence tested like any other external command.

⁹² For example, the ability to handle https:// URLs.

Do be careful in what your tests (and examples) actually test. Bad practice seen in distributed packages include:

• It is not reasonable to test the time taken by a command: you cannot know how fast or how heavily loaded an R platform might be. At best you can test a ratio of times, and even that is fraught with difficulties and not advisable: for example, the garbage collector may trigger at unpredictable times following heuristics that may change without notice.

• Do not test the exact format of R messages (from R itself or from other packages): They change, and they can be translated.

  Packages have even tested the exact format of system error messages, which are platform-dependent and perhaps locale-dependent. For example, in late 2021 libcurl changed its warning/error messages, including when URLs are not found.

• Do not test for the absence of warnings (something users of testthat are fond of). Future changes in either R or packages you make use of can create new warnings, and your tests should not make these into errors. (Some deprecation notices may be intended to remain as warnings for a long time.)

• If you use functions such as View, remember that in testing there is no one to look at the output. It is better to use something like one of

  if(interactive()) View(obj) else print(head(obj))
  if(interactive()) View(obj) else str(obj)

• Be careful when comparing file paths. There can be multiple paths to a single file, and some of these can be very long character strings. If possible canonicalize paths before comparisons, but study ?normalizePath to be aware of the pitfalls.

• Only test the accuracy of results if you have done a formal error analysis. Things such as checking that probabilities numerically sum to one are silly: numerical tests should always have a tolerance. That the tests on your platform achieve a particular tolerance says little about other platforms. R is configured by default to make use of long doubles where available, but they may not be available or be too slow for routine use. Most R platforms use ix86 or x86_64 CPUs: these may use extended precision registers on some but not all of their FPU instructions. Thus the achieved precision can depend on the compiler version and optimization flags—our experience is that 32-bit builds tend to be less precise than 64-bit ones. But not all platforms use those CPUs, and not all⁹³ which use them configure them to allow the use of extended precision. In particular, current ARM CPUs do not have extended precision nor long doubles, and clang currently has long double the same as double on all ARM CPUs. On the other hand some CPUs have higher-precision modes which may be used for long double, notably 64-bit PowerPC and Sparc.

  If you must try to establish a tolerance empirically, configure and build R with --disable-long-double and use appropriate compiler flags (such as -ffloat-store and -fexcess-precision=standard for gcc, depending on the CPU type⁹⁴) to mitigate the effects of extended-precision calculations. The platform most often seen to give different numerical results is arm64 macOS, so be sure to include that in any empirical determination.

  Tests which involve random inputs or non-deterministic algorithms should normally set a seed or be tested for many seeds.

• Tests should use options(warn = 1) as reporting

  There were 22 warnings (use warnings() to see them)

  is pointless, especially for automated checking systems.

• If your package uses dates/times, ensure that it works in all timezones, especially those near boundaries (problems have most often be seen in Europe/London (zero offset in Winter) and Pacific/Auckland, near enough the International Date line) and with offsets not in whole hours (Adelaide, Chatham Islands, …). More extreme examples are Africa/Conakry (permanent UTC), Asia/Calcutta (no DST, permanent half-hour offset) and Pacific/Kiritimati(no DST, more than 12 hours ahead of UTC).

⁹³ Not doing so is the default on Windows, overridden for the R executables.

⁹⁴ These are not needed for the default compiler settings on x86_64 but are likely to be needed on ix86.

1.6.1 PDF size

There are a several tools available to reduce the size of PDF files: often the size can be reduced substantially with no or minimal loss in quality. Not only do large files take up space: they can stress the PDF viewer and take many minutes to print (if they can be printed at all).

qpdf (https://qpdf.sourceforge.io/) can compress losslessly. It is fairly readily available (e.g. it is included in rtools, has packages in Debian/Ubuntu/Fedora, and is installed as part of the CRAN macOS distribution of R). R CMD build has an option to run qpdf over PDF files under inst/doc and replace them if at least 10Kb and 10% is saved. The full path to the qpdf command can be supplied as environment variable R_QPDF (and is on the CRAN binary of R for macOS). It seems MiKTeX does not use PDF object compression and so qpdf can reduce considerably the sizes of files it outputs: MiKTeX’s defaults can be overridden by code in the preamble of an Sweave or LaTeX file — see how this is done for the R reference manual at https://svn.r-project.org/R/trunk/doc/manual/refman.top.

Other tools can reduce the size of PDFs containing bitmap images at excessively high resolution. These are often best re-generated (for example Sweave defaults to 300 ppi, and 100–150 is more appropriate for a package manual). These tools include Adobe Acrobat (not Reader), Apple’s Preview⁹⁵ and Ghostscript (which converts PDF to PDF by

⁹⁵ Select ‘Save as’, and select ‘Reduce file size’ from the ‘Quartz filter’ menu’: this can be accessed in other ways, for example by Automator.

ps2pdf options -dAutoRotatePages=/None -dPrinted=false in.pdf out.pdf

and suitable options might be

-dPDFSETTINGS=/ebook
-dPDFSETTINGS=/screen

See https://ghostscript.readthedocs.io/en/latest/VectorDevices.html for more such and consider all the options for image downsampling). There have been examples in CRAN packages for which current versions of Ghostscript produced much bigger reductions than earlier ones (e.g. at the upgrades from 9.50 to 9.52, from 9.55 to 9.56 and then to 10.00.0).

We come across occasionally large PDF files containing excessively complicated figures using PDF vector graphics: such figures are often best redesigned or failing that, output as PNG files.

Option --compact-vignettes to R CMD build defaults to value qpdf: use both to try harder to reduce the size, provided you have Ghostscript available (see the help for tools::compactPDF).

1.6.2 Check timing

There are several ways to find out where time is being spent in the check process. Start by setting the environment variable _R_CHECK_TIMINGS_ to 0. This will report the total CPU times (not Windows) and elapsed times for installation and running examples, tests and vignettes, under each sub-architecture if appropriate. For tests and vignettes, it reports the time for each as well as the total.

Setting _R_CHECK_TIMINGS_ to a positive value sets a threshold (in seconds elapsed time) for reporting timings.

If you need to look in more detail at the timings for examples, use option --timings to R CMD check (this is set by --as-cran). This adds a summary to the check output for all the examples with CPU or elapsed time of more than 5 seconds. It produces a file mypkg.Rcheck/mypkg-Ex.timings containing timings for each help file: it is a tab-delimited file which can be read into R for further analysis.

Timings for the tests and vignette runs are given at the bottom of the corresponding log file: note that log files for successful vignette runs are only retained if environment variable _R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_ is set to a true value.

1.6.3 Encoding issues

The issues in this subsection have been much alleviated by the change in R 4.2.0 to running the Windows port of R in a UTF-8 locale where available. However, Windows users might be running an earlier version of R on an earlier version of Windows which does not support UTF-8 locales.

Care is needed if your package contains non-ASCII text, and in particular if it is intended to be used in more than one locale. It is possible to mark the encoding used in the DESCRIPTION file and in .Rd files, as discussed elsewhere in this manual.

First, consider carefully if you really need non-ASCII text. Some users of R will only be able to view correctly text in their native language group (e.g. Western European, Eastern European, Simplified Chinese) and ASCII.⁹⁶. Other characters may not be rendered at all, rendered incorrectly, or cause your R code to give an error. For .Rd documentation, marking the encoding and including ASCII transliterations is likely to do a reasonable job. The set of characters which is commonly supported is wider than it used to be around 2000, but non-Latin alphabets (Greek, Russian, Georgian, …) are still often problematic and those with double-width characters (Chinese, Japanese, Korean, emoji) often need specialist fonts to render correctly.

⁹⁶ except perhaps some special characters such as backslash and hash which may be taken over for currency symbols.

Several CRAN packages have messages in their R code in French (and a few in German). A better way to tackle this is to use the internationalization facilities discussed elsewhere in this manual.

Function showNonASCIIfile in package tools can help in finding non-ASCII bytes in files.

There is a portable way to have arbitrary text in character strings (only) in your R code, which is to supply them in Unicode as \uxxxx escapes (or, rarely needed except for emojis, \Uxxxxxxxx escapes). If there are any characters not in the current encoding the parser will encode the character string as UTF-8 and mark it as such. This applies also to character strings in datasets: they can be prepared using \uxxxx escapes or encoded in UTF-8 in a UTF-8 locale, or even converted to UTF-8 via iconv(). If you do this, make sure you have R (>= 2.10) (or later) in the Depends field of the DESCRIPTION file.

R sessions running in non-UTF-8 locales will if possible re-encode such strings for display (and this is done by RGui on older versions of Windows, for example). Suitable fonts will need to be selected or made available⁹⁷ both for the console/terminal and graphics devices such as X11() and windows(). Using postscript or pdf will choose a default 8-bit encoding depending on the language of the UTF-8 locale, and your users would need to be told how to select the encoding argument.

⁹⁷ Typically on a Unix-alike this is done by telling fontconfig where to find suitable fonts to select glyphs from.

Note that the previous two paragraphs only apply to character strings in R code. Non-ASCII characters are particularly prevalent in comments (in the R code of the package, in examples, tests, vignettes and even in the NAMESPACE file) but should be avoided there. Most commonly people use the Windows extensions to Latin-1 (often directional single and double quotes, ellipsis, bullet and en and em dashes) which are not supported in strict Latin-1 locales nor in CJK locales on Windows. A surprisingly common misuse is to use a right quote in don't instead of the correct apostrophe.

Datasets can include marked UTF-8 or Latin-1 character strings. As R is nowadays unlikely to be run in a Latin-1 or Windows’ CP1252 locale, for performance reasons these should be converted to UTF-8.

If you want to run R CMD check on a Unix-alike over a package that sets a package encoding in its DESCRIPTION file and do not use a UTF-8 locale you may need to specify a suitable locale via environment variable R_ENCODING_LOCALES. The default is equivalent to the value

"latin1=en_US:latin2=pl_PL:UTF-8=en_US.UTF-8:latin9=fr_FR.iso885915@euro"

(which is appropriate for a system based on glibc: macOS requires latin9=fr_FR.ISO8859-15) except that if the current locale is UTF-8 then the package code is translated to UTF-8 for syntax checking, so it is strongly recommended to check in a UTF-8 locale.

1.6.4 Portable C and C++ code

Writing portable C and C++ code is mainly a matter of observing the standards (C99, C++14 or where declared C++11/17/20) and testing that extensions (such as POSIX functions) are supported. Do make maximal use of your compiler diagnostics — this typically means using flags -Wall and -pedantic for both C and C++ and additionally -Werror=implicit-function-declaration and -Wstrict-prototypes for C (on some platforms and compiler versions) these are part of -Wall or -pedantic).

C++ standards: From version 3.6.0 (3.6.2 on Windows), R defaulted to C++11 where available⁹⁸; from R 4.1.0 to C++14 and from R 4.3.0 to C++17 (where available). However, in earlier versions the default standard was that of the compiler used, often C++98 or C++14, and the default is likely to change in future. For maximal portability a package should either specify a standard (see Using C++ code) or be tested under all of C++11, C++98, C++14 and C++17. (Specifying C++14 or later will limit portability.)

⁹⁸ which it is on all known platforms, and is required as from R 4.0.0

Note that the ‘TR1’ C++ extensions are not part of any of these standards and the <tr1/name> headers are not supplied by some of the compilers used for R, including on macOS. (Use the C++11 versions instead.)

A common error is to assume recent versions of compilers or OSes. In production environments ‘long term support’ versions of OSes may be in use for many years,⁹⁹ and their compilers may not be updated during that time. For example, GCC 4.8 was still in use in 2022 and could be (in RHEL 7) until 2028: that supports neither C++14 nor C++17.

⁹⁹ Ubuntu provides 5 years of support (but people were running 14.04 after 7 years) and RHEL provides 10 years full support and up to 14 with extended support.

¹⁰⁰ This is seen on Linux, Solaris and FreeBSD, although each has other ways to turn on all extensions, e.g. defining _GNU_SOURCE, __EXTENSIONS__ or _BSD_SOURCE: the GCC compilers by default define _GNU_SOURCE unless a strict standard such as -std=c99 is used. On macOS extensions are declared unless one of these macros is given too small a value.

The POSIX standards only require recently-defined functions to be declared if certain macros are defined with large enough values, and on some compiler/OS combinations¹⁰⁰ they are not declared otherwise. So you may need to include something like one of

#define _XOPEN_SOURCE 600

or

#ifdef __GLIBC__
# define _POSIX_C_SOURCE 200809L
#endif

before any headers. (strdup, strncasecmp and strnlen are such functions – there were several older platforms which did not have the POSIX 2008 function strnlen.)

However, some common errors are worth pointing out here. It can be helpful to look up functions at https://cplusplus.com/reference/ or https://en.cppreference.com/w/ and compare what is defined in the various standards.

More care is needed for functions such as mallinfo which are not specified by any of these standards—hopefully the man page on your system will tell you so. Searching online for such pages for various OSes (preferably at least Linux and macOS, and the FreeBSD manual pages at https://man.freebsd.org/cgi/man.cgi allow you to select many OSes) should reveal useful information but a configure script is likely to be needed to check availability and functionality.

Both the compiler and OS (via system header files, which may differ by architecture even for nominally the same OS) affect the compilability of C/C++ code. Compilers from the GCC, LLVM (clang and flang) Intel and Oracle Developer Studio suites have been used with R, and both LLVM clang and Oracle have more than one implementation of C++ headers and library. The range of possibilities makes comprehensive empirical checking impossible, and regrettably compilers are patchy at best on warning about non-standard code.

• Mathematical functions such as sqrt are defined in C++11 for floating-point arguments: float, double, long double and possibly more. The standard specifies what happens with an argument of integer type but this is not always implemented, resulting in a report of ‘overloading ambiguity’: this was commonly seen on Solaris, but for pow also seen on macOS and other platforms using clang++.

  A not-uncommonly-seen problem is to mistakenly call floor(x/y) or ceil(x/y) for int arguments x and y. Since x/y does integer division, the result is of type int and ‘overloading ambiguity’ may be reported. Some people have (pointlessly) called floor and ceil on arguments of integer type, which may have an ‘overloading ambiguity’.

  A surprising common misuse is things like pow(10, -3): this should be the constant 1e-3. Note that there are constants such as M_SQRT2 defined via Rmath.h¹⁰¹ for sqrt(2.0), frequently mis-coded as sqrt(2).

• Function fabs is defined only for floating-point types, except in C++11 and later which have overloads for std::fabs in <cmath> for integer types. Function abs is defined in C99’s <stdlib.h> for int and in C++’s <cstdlib> for integer types, overloaded in <cmath> for floating-point types. C++11 has additional overloads for std::abs in <cmath> for integer types. The effect of calling abs with a floating-point type is implementation-specific: it may truncate to an integer. For clarity and to avoid compiler warnings, use abs for integer types and fabs for double values, and when using C++ include <cmath> and use the std:: prefix.

• It is an error (and make little sense, although has been seen) to call macros/functions isnan, isinf and isfinite for integer arguments: a few compilers give a compilation error. Function finite is obsolete, and some compilers will warn about its use¹⁰².

• The GNU C/C++ compilers support a large number of non-portable extensions. For example, INFINITY (which is a float value in C99 and C++11), for which R provides the portable double value R_PosInf (and R_NegInf for -INFINITY). And NAN¹⁰³ is just one NaN float value: for use with R, NA_REAL is often what is intended, but R_NaN is also available.

  Some (but not all) extensions are listed at https://gcc.gnu.org/onlinedocs/gcc/C-Extensions.html and https://gcc.gnu.org/onlinedocs/gcc/C_002b_002b-Extensions.html.

  Other GNU extensions which have bitten package writers are the use of non-portable characters such as $ in identifiers and use of C++ headers under ext.

• Including C-style headers in C++ code is not portable. Including the legacy header¹⁰⁴ math.h in C++ code may conflict with cmath which may be included by other headers. In C++11, functions like sqrt and isnan are defined for double arguments in math.h and for a range of types including double in cmath. Similar issues have been seen for stdlib.h and cstdlib. Including the C++ header first used to be a sufficient workaround but for some 2016 compilers only one could be included.

• Be careful to include the headers which define the functions you use. Some compilers/OSes include other system headers in their headers which are not required by the standards, and so code may compile on such systems and not on others. (A prominent example is the C++ header <random> which is indirectly included by <algorithm> by g++. Another issue is the C header <time.h> which is included by other headers on Linux and Windows but not macOS.) g++ 11 often needs explicit inclusion of the C++ headers <limits> (for numeric_limits) or <exception> (for set_terminate and similar), whereas earlier versions included these in other headers. g++ 13 requires the explicit inclusion of <cstdint> for types such as uint32_t which was previously included implicitly. (For more such, see https://gcc.gnu.org/gcc-13/porting_to.html.)

  Note that malloc, calloc, realloc and free are defined by C99 in the header stdlib.h and (in the std:: namespace) by C++ header cstdlib. Some earlier implementations used a header malloc.h, but that is not portable and does not exist on macOS.

  This also applies to types such as ssize_t. The POSIX standards say that is declared in headers unistd.h and sys/types.h, and the latter is often included indirectly by other headers on some but not all systems.

  Similarly for constants: for example SIZE_MAX is defined in stdint.h alongside size_t.

• Some headers are not portable: we have just mentioned malloc.h and often CRAN submissions attempt to use endian.h. The latter is a glibc extension: some OSes have machine/endian.h or sys/endian.h but some have neither.

• Use #include "my.h" not #include <my.h> for headers in your package. The second form is intended for system headers and the search order for such headers is platform-dependent (and may not include the current directory). For extra safety, name headers in a way that cannot be confused with a system header so not, for example, types.h.

• For C++ code, be careful to specify namespaces where needed. Many functions are defined by the standards to be in the std namespace, but g++ puts many such also in the C++ main namespace. One way to do so is to use declarations such as

  using std::floor;

  but it is usually preferable to use explicit namespace prefixes in the code.

  Examples seen in CRAN packages include

  abs acos atan bind calloc ceil div exp fabs floor fmod free log malloc
  memcpy memset pow printf qsort round sin sprintf sqrt strcmp strcpy
  strerror strlen strncmp strtol tan trunc

  This problem is less common than it used to be, but in 2019 LLVM clang did not have bind in the main namespace. Also seen has been type size_t defined only in the std namespace.

  NB: These functions are only guaranteed to be in the std namespace if the correct C++ header is included, e.g. <cmath> rather than <math.h>.

  If you define functions in C++ which are inspired by later standards, put them in a namespace and refer to them using the namespace. We have seen conflicts with std::make_unique from C++14 and std::byte, std::data, std::sample and std::size from C++17.

• In C++ code

  using namespace std;

  is not good practice, and has caused platform-dependent errors if included before headers, especially system headers (which may be included by other headers). The best practice is to use explicit std:: prefixes for all functions declared by the C++ standard to be in that namespace. It is an error to use using namespace std before including any C++ headers, and some recent compilers will warn if this is done.

• Some C++ compilers refuse to compile constructs such as

        if(ptr > 0) { ....}

  which compares a pointer to the integer 0. This could just use if(ptr) (pointer addresses cannot be negative) but if needed pointers can be tested against nullptr (C++11) or NULL.

• Macros defined by the compiler/OS can cause problems. Identifiers starting with an underscore followed by an upper-case letter or another underscore are reserved for system macros and should not be used in portable code (including not as guards in C/C++ headers). Other macros, typically upper-case, may be defined by the compiler or system headers and can cause problems. Some of these can be avoided by defining _POSIX_C_SOURCE before including any system headers, but it is better to only use all-upper-case names which have a unique prefix such as the package name.

• typedefs in OS headers can conflict with those in the package: examples have included ulong, index_t, single and thread. (Note that these may conflict with other uses as identifiers, e.g. defining a C++ function called single.) The POSIX standard reserves (in §2.2.2) all identifiers ending in _t.

• Some compilers do not allow a space between -D and the macro to be defined. Similarly for -U.

• If you use OpenMP, check carefully that you have followed the advice in the subsection on OpenMP support. In particular, any use of OpenMP in C/C++ code will need to use

  #ifdef _OPENMP
  # include <omp.h>
  #endif

  Any use of OpenMP functions, e.g. omp_set_num_threads, also needs to be conditioned. To avoid incessant warnings such as

  warning: ignoring #pragma omp parallel [-Wunknown-pragmas]

  uses of such pragmas should also be conditioned (or commented out if they are used in code in a package not enabling OpenMP on any platform).

  Do not hardcode -lgomp: not only is that specific to the GCC family of compilers, using the correct linker flag often sets up the run-time path to the library.

• Package authors commonly assume things are part of C/C++ when they are not: the most common example is POSIX¹⁰⁵ function strdup. The most common C library on Linux, glibc, will hide the declarations of such extensions unless a ‘feature-test macro’ is defined before (almost) any system header is included. So for strdup you need

  #define _POSIX_C_SOURCE 200809L
  ...
  #include <string.h>
  ...
  strdup call(s)

  where the appropriate value can be found by man strdup on Linux. (Use of strncasecmp is similar.)

  However, modes of gcc with ‘GNU EXTENSIONS’ (which are the default, either -std=gnu99 or -std=gnu11) declare enough macros to ensure that missing declarations are rarely seen.

  This applies also to constants such as M_PI and M_LN2, which are part of the X/Open standard: to use these define _XOPEN_SOURCE before including any headers, or include the R header Rmath.h.

• Using alloca portably is tricky: it is neither an ISO C/C++ nor a POSIX function. An adequately portable preamble is

  #ifdef __GNUC__
  /* Includes GCC, clang and Intel compilers */
  # undef alloca
  # define alloca(x) __builtin_alloca((x))
  #elif defined(__sun) || defined(_AIX)
  /* this was necessary (and sufficient) for Solaris 10 and AIX 6: */
  # include <alloca.h>
  #endif

• Compiler writers feel free to implement features from later standards than the one specified, so for example they may implement or warn on C++14/17/20 features when C++11 is specified. Portable code will not use such features – it can be hard to know what they are but the most common warnings are

  'register' storage class specifier is deprecated and incompatible with C++17
  
  ISO C++11 does not allow conversion from string literal to 'char *'

  (where conversion should be to const char *). Keyword register was not mentioned in C++98, deprecated in C++11 and removed in C++17.

  There are quite a lot of other C++98 features deprecated in C++11 and removed in C++17, and LLVM clang 9 and later warn about them (and as from version 16 they have been removed). Examples include bind1st/bind2nd (use std::bind or lambdas¹⁰⁶) std::auto_ptr (replaced by std::unique_ptr), std::mem_fun_ref and std::ptr_fun.

• Later versions of standards may add reserved words: for example bool, false and true became keywords in C23 and are no longer available as variable names. As noted above, C++17 uses byte, data, sample and size.

  So avoid common words and keywords from other programming languages.

• Be careful about including C headers in C++ code. Issues include

  • Use of the register storage class specifier (see the previous but one item).
  • The C99 keyword restrict is not part of¹⁰⁷ any C++ standard and is rejected by some C++ compilers.
  • Inclusion by such headers of C-style headers such as math.h (see above).

  The most portable way to interface to other software with a C API is to use C code (which can normally be mixed with C++ code in a package).

• Include only what is essential in extern "C" {} blocks in C++ code. In particular it is not portable to include R headers in such blocks (although they are themselves C code, they may include C++ system headers and the public ones already enclose their declarations in such a block). And maintainers have include R headers from other headers included in such a block.

• reinterpret_cast in C++ is not safe for pointers: for example the types may have different alignment requirements. Use memcpy to copy the contents to a fresh variable of the destination type.

• Avoid platform-specific code if at all possible, but if you need to test for a platform ensure that all platforms are covered. For example, __unix__ is not defined on all Unix-alikes, in particular not on macOS. A reasonably portable way to condition code for a Unix-alike is

  #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
  #endif

  but

  #ifdef _WIN32
  // Windows-specific code
  # if defined(_M_ARM64) || defined(__aarch64__)
  // for ARM
  # else
  // for Intel
  # endif
  #else
  // Unix-alike code
  #endif

  would be better. For a Unix-alike it is much better to use configure to test for the functionality needed than make assumptions about OSes (and people all too frequently forget R is used on platforms other than Linux, Windows and macOS — and some forget macOS).

• Headers in subdirectories are often not portable. For C++, this includes bits/, tr1/ and tr2/, none of which exist on macOS (and ext/ exists there but with different content from g++-based platforms). Header bits/stdc++.h is both not portable and not recommended for end-user code even on platforms which include it.

• Be careful if using malloc or calloc. First, their return value must always be checked to see if the allocation succeeded – it is almost always easier to use R’s R_Calloc, which does check. Second, the first argument is of type size_t¹⁰⁸ and some recent compilers warn about passing int (signed) arguments (which could get promoted to ridiculously large values).

• For C code, consider using the flag -Wstrict-prototypes which is supported by gcc and LLVM and Apple clang. This has found quite a number of errors where functions have been declared without arguments and is likely to become the default in future compilers. (It already is for Apple clang and for LLVM clang in C23 mode.) Note that using f() for a function without any parameters was deprecated in C99 and C11, but it expected to be non-deprecated in C23. However, f(void) is supported by all standards and avoids any uncertainty.

  LLVM clang has a separate warning -Wdeprecated-non-prototype which is enabled by -Wstrict-prototypes. This warns on K&R-style usage, which will not be accepted in C23.

• Several C entry points are warned against in their man pages on most systems, often in very strong terms such as ‘Do not use these functions’. macOS has started to warn¹⁰⁹ if these are used for sprintf, vsprintf, gets, mktemp, tempmam and tmpnam. It is highly recommended that you use safer alternatives (on any platform) but the warning can be avoided by defining _POSIX_C_SOURCE to for example 200809L before including the (C or C++) header which defines them. (However, this may hide other extensions.)

• Compilers may interpret comments in source code, so it is necessary to remove any intended for a compiler to interpret. The main example has been comments for Visual Fortran (as the Intel Fortran compiler has been known on Windows¹¹⁰) like

  !DEC$ ATTRIBUTES DLLEXPORT,C,REFERENCE,ALIAS:'kdenestmlcvb' :: kdenestmlcvb

  which are interpreted by Intel Fortran on all platforms (and are inappropriate for use with R on Windows). gfortran has similar forms starting with !GCC$.

• The C++ new operator takes argument std::size_t size, which is unsigned. Using a signed integer type such as int may lead to compiler warnings such as

  warning: argument 1 value '18446744073709551615' exceeds maximum object
    size 9223372036854775807  [-Walloc-size-larger-than=]

  (especially if LTO is used). So don’t do that!

• Some authors feel the need to print (using Rprintf or similar) vector lengths or indices which are of type R_xlen_t. That may be a 32-bit or (most commonly) 64-bit type but which integer type it is mapped to is platform-specific. The safest way is to cast the length to double and use a double format. So one could use something like

      SEXP Robj; R_xlen_t nelem;
      Rf_error("Actual: %0.f; Expected %0.f\n", (double) XLENGTH(Robj), (double) nelem);

  (This could print to full precision, lengths well beyond the address space limits of current OSes, let alone practical limits.)

  If you do want to use an integer format, be aware that R_xlen_t is implemented by the int, long or long long type on current platforms and even on 64-bit ones need not be the same type as int64_t. So the values will need to be cast to the type assumed by the format (and %lld was not supported on Windows until R 4.2.0).

• Variadic macros in C or C++ only portably allow a non-zero number of arguments, although some compilers have allowed zero, often with a warning. The latter was standardized in C++20 using the __VA_OPT__ macro. C23 will also allow zero arguments in a similar way.

¹⁰¹ often taken from the toolchain’s headers.

¹⁰² at the time of writing arm64 macOS both warned and did not supply a prototype in math.h which resulted in a compilation error.

¹⁰³ also part of C++11 and later.

¹⁰⁴ which often is the same as the header included by the C compiler, but some compilers have wrappers for some of the C headers.

¹⁰⁵ Although this is expected to be part of C23, full support of that is years away.

¹⁰⁶ https://stackoverflow.com/questions/32739018/a-replacement-for-stdbind2nd

¹⁰⁷ it is allowed but ignored in system headers.

¹⁰⁸ an unsigned 64-bit integer type on recent R platforms.

¹⁰⁹ when using the macOS 13 SDK with a deployment target of macOS 13.

¹¹⁰ and at one time as DEC Fortran, hence the DEC.

Some additional information for C++ is available at https://journal.r-project.org/archive/2011-2/RJournal_2011-2_Plummer.pdf by Martyn Plummer.

1.6.5 Common symbols

Most OSes (including all those commonly used for R) have the concept of ‘tentative definitions’ where global C variables are defined without an initializer. Traditionally the linker resolves all tentative definitions of the same variable in different object files to the same object, or to a non-tentative definition. However, gcc 10¹¹¹ and LLVM clang 11¹¹² changed their default so that tentative definitions cannot be merged and the linker will give an error if the same variable is defined in more than one object file. To avoid this, all but one of the C source files should declare the variable extern — which means that any such variables included in header files need to be declared extern. A commonly used idiom (including by R itself) is to define all global variables as extern in a header, say globals.h (and nowhere else), and then in one (and one only) source file use

¹¹¹ see https://gcc.gnu.org/gcc-10/porting_to.html.

¹¹² See https://prereleases.llvm.org/11.0.0/rc2/tools/clang/docs/ReleaseNotes.html#modified-compiler-flags.

#define extern
# include "globals.h"
#undef extern

A cleaner approach is not to have global variables at all, but to place in a single file common variables (declared static) followed by all the functions which make use of them: this may result in more efficient code.

The ‘modern’ behaviour can be seen¹¹³ by using compiler flag -fno-common as part of CFLAGS in earlier versions of gcc and clang.

¹¹³ In principle this could depend on the OS, but has been checked on Linux and macOS.

-fno-common is said to be particularly beneficial for ARM CPUs.

This is not pertinent to C++ which does not permit tentative definitions.

1.6.6 C++17 issues

R 4.3.0 and later default to C++17 when compiling C++, and that finally removed many C++98 features which were deprecated as long ago as C++11. Compiler/runtime authors have been slow to remove these, but LLVM clang with its libc++ runtime library finally started to do so in 2023 – some others warn but some do not.

The principal offender is the ‘Boost’ collection of C++ headers and libraries. There are two little-documented ways to work around aspects of its outdated code. One is to add

-D_HAS_AUTO_PTR_ETC=0

to PKG_CPPLAGS in src/Makevars, src/Makevars.win and src/Makevars.ucrt. This covers the removal of

std::auto_ptr
std::unary_function
std::binary_function
std::random_shuffle
std::binder1st
std::binder2nd

with most issues seen with code that includes boost/functional.hpp, usually indirectly.

A rarer issue is the use of illegal values for enum types, usually negative ones such as

BOOST_MPL_AUX_STATIC_CAST(AUX_WRAPPER_VALUE_TYPE, (value - 1));

in boost/mpl/aux_/integral_wrapper.hpp. Adding

 -Wno-error=enum-constexpr-conversion

to PKG_CXXFLAGS will allow this, but that flag is only accepted by recent versions of LLVM clang (and will not be in future) so needs a configure test.

Pre=built versions of current clang/libc++ are usually available from https://github.com/llvm/llvm-project/releases for a wide range of platforms (but the Windows builds there are not compatible with Rtools and the macOS ones are unsigned). To select libc++ add -stdlib=libc++ to CXX, for example by having

CXX="/path/to/clang/clang++ -std=gnu++17 -stdlib=libc++"

in ~/.R/Makevars.

Another build for Windows which may be sufficiently compatible with Rtools can be found at https://github.com/mstorsjo/llvm-mingw: this uses libc++.

1.6.7 Portable Fortran code

For many years almost all known R platforms used gfortran as their Fortran compiler, but now there are LLVM and ‘classic’ flang and the Intel compilers ifort¹¹⁴ and ifx are now free-of-change.

¹¹⁴ discontinued in 2023.

There is still a lot of Fortran code in CRAN packages which predates Fortran 77. Modern Fortran compilers are being written to target a minimum standard of Fortran 2018. and it is desirable that Fortran code in packages complies with that standard. For gfortran this can be checked by adding -std=f2018 to FFLAGS. The most commonly seen issues are

• The use of DFLOAT, which was superseded by DBLE in Fortran 77. Also, use of DCMPLX, DCONJG, DIMAG and similar.

• Use of what gfortran calls ‘Fortran 2018 deleted features’, although most were ‘deleted’ in earlier standards: those itemized here were deleted in Fortran 2008. (In the Fortran standards ‘deleted’ means features that compilers are not required to implement.) These include

  • Arithmetic IF statements.
  • DO loops which are not terminated with a END DO or CONTINUE statement. (Unlabelled DO loops terminated by END DO are preferred for readability.)
  • Labelled DO loops sharing a terminating CONTINUE statement.

• The use of GNU Fortran extensions. Some are listed at https://gcc.gnu.org/onlinedocs/gfortran/Extensions-implemented-in-GNU-Fortran.html. Others which have caused problems include etime, getpid, isnan¹¹⁵ and sizeof.

  One that frequently catches package writers is that it allows out-of-order declarations: in standard-conformant Fortran variables must be declared (explicitly or implicitly) before use in other declarations such as dimensions.

¹¹⁵ There is a portable way to do this in Fortran 2003 (ieee_is_nan() in module ieee_arithmetic), but that was not supported in the versions 4.x of GNU Fortran. A pretty robust alternative is to test if(my_var /= my_var).

Unfortunately this flags extensions such as DOUBLE COMPLEX and COMPLEX*16. R has tested that DOUBLE COMPLEX works and so is preferred to COMPLEX*16. (One can also use something like COMPLEX(KIND=KIND(0.0D0)).)

GNU Fortran 10 and later give a compilation error for the previously widespread practice of passing a Fortran array element where an array is expected, or a scalar instead of a length-one array. See https://gcc.gnu.org/gcc-10/porting_to.html. As do the Intel Fortran compilers, and they can be stricter.

The use of IMPLICIT NONE is highly recommended – Intel compilers with -warn will warn on variables without an explicit type.

Common non-portable constructions include

• The use of Fortran types such as REAL(KIND=8) is very far from portable. According to the standards this merely enumerates different supported types, so DOUBLE PRECISION might be REAL(KIND=3) (and is on an actual compiler). Even if for a particular compiler the value indicates the size in bytes, which values are supported is platform-specific — for example gfortran supports values of 4 and 8 on all current platforms and 10 and 16 on a few (but not for example on all arm CPUs).

  The same applies to INTEGER(KIND=4) and COMPLEX(KIND=16).

  Many uses of integer and real variable in Fortran code in packages will interwork with C (for example .Fortran is written in C), and R has checked that INTEGER and DOUBLE PRECISION correspond to the C types int and double. To make this explicit, from Fortran 2003 one can use the named constants c_int, c_double and c_double_complex from module iso_c_binding.

• The Intel compilers only recognize the extensions .f (fixed-form) and .f90 (free-form) and not .f95. R CMD INSTALL works around this for packages without a src/Makefile.

• Use of extensions .F and .F90 to indicate source code to be preprocessed: the preprocessor used is compiler-specific and may or may not be cpp. Compilers may even preprocess files with extension .f or .f90 (Intel does).

• Fixed form Fortran (with extension .f) should only use 72 columns, and free-form at most 132 columns. This includes trailing comments. Over-long lines may be silently truncated or give a warning.

• Tabs are not part of the Fortran character set: compilers tend to accept them but how they are interpreted is compiler-specific.

• Fortran-66-style Hollerith constants.

As well as ‘deleted features’, Fortran standards have ‘obsolescent features’. These are similar to ‘deprecated’ in other languages, but the Fortran standards committee has said it will only move them to ‘deleted’ status when they are no longer much used. These include

• ENTTRY statements.
• FORALL statements.
• Labelled DO statements.
• COMMON and EQUIVALENCE statements, and BLOCK DATA units.
• Computed GOTO statements, replaced by SELECT CASE.
• Statement functions.
• DATA statements after executable statements.
• Specific (rather than generic) names for intrinsic functions.

gfortran with option -std=f2018 will warn about these: R will report only in the installation log.

1.6.8 Binary distribution

If you want to distribute a binary version of a package on Windows or macOS, there are further checks you need to do to check it is portable: it is all too easy to depend on external software on your own machine that other users will not have.

For Windows, check what other DLLs your package’s DLL depends on (‘imports’ from in the DLL tools’ parlance). A convenient GUI-based tool to do so is ‘Dependency Walker’ (https://www.dependencywalker.com/) for both 32-bit and 64-bit DLLs – note that this will report as missing links to R’s own DLLs such as R.dll and Rblas.dll. The command-line tool objdump in the appropriate toolchain will also reveal what DLLs are imported from. If you use a toolchain other than one provided by the R developers or use your own makefiles, watch out in particular for dependencies on the toolchain’s runtime DLLs such as libgfortran, libstdc++ and libgcc_s.

For macOS, using R CMD otool -L on the package’s shared object(s) in the libs directory will show what they depend on: watch for any dependencies in /usr/local/lib or /usr/local/gfortran/lib, notably libgfortran.?.dylib and libquadmath.0.dylib. (For ways to fix these, see Building binary packages in R Installation and Administration.)

Many people (including the CRAN package repository) will not accept source packages containing binary files as the latter are a security risk. If you want to distribute a source package which needs external software on Windows or macOS, options include

• To arrange for installation of the package to download the additional software from a URL, as e.g. package Cairo used to.
• To negotiate with Tomas Kalibera to include Windows software in Rtools or with Simon Urbanek to include macOS software in his ‘recipes’ system.
• (For CRAN.) To negotiate with Uwe Ligges to host the additional components on WinBuilder, and write a configure.win file to install them.

Be aware that license requirements you may require you to supply the sources for the additional components (and will if your package has a GPL-like license).

1.7 Diagnostic messages

Diagnostic messages can be made available for translation, so it is important to write them in a consistent style. Using the tools described in the next section to extract all the messages can give a useful overview of your consistency (or lack of it). Some guidelines follow.

• Messages are sentence fragments, and not viewed in isolation. So it is conventional not to capitalize the first word and not to end with a period (or other punctuation).

• Try not to split up messages into small pieces. In C error messages use a single format string containing all English words in the messages.

  In R error messages do not construct a message with paste (such messages will not be translated) but via multiple arguments to stop or warning, or via gettextf.

• Do not use colloquialisms such as “can’t” and “don’t”.

• Conventionally single quotation marks are used for quotations such as

  'ord' must be a positive integer, at most the number of knots

  and double quotation marks when referring to an R character string or a class, such as

  'format' must be "normal" or "short" - using "normal"

  Since ASCII does not contain directional quotation marks, it is best to use ’ and let the translator (including automatic translation) use directional quotations where available. The range of quotation styles is immense: unfortunately we cannot reproduce them in a portable texinfo document. But as a taster, some languages use ‘up’ and ‘down’ (comma) quotes rather than left or right quotes, and some use guillemets (and some use what Adobe calls ‘guillemotleft’ to start and others use it to end).

  In R messages it is also possible to use sQuote or dQuote as in

          stop(gettextf("object must be of class %s or %s",
                        dQuote("manova"), dQuote("maov")),
               domain = NA)

• Occasionally messages need to be singular or plural (and in other languages there may be no such concept or several plural forms – Slovenian has four). So avoid constructions such as was once used in library

  if((length(nopkgs) > 0) && !missing(lib.loc)) {
      if(length(nopkgs) > 1)
          warning("libraries ",
                  paste(sQuote(nopkgs), collapse = ", "),
                  " contain no packages")
      else
          warning("library ", paste(sQuote(nopkgs)),
                  " contains no package")
  }

  and was replaced by

  if((length(nopkgs) > 0) && !missing(lib.loc)) {
      pkglist <- paste(sQuote(nopkgs), collapse = ", ")
      msg <- sprintf(ngettext(length(nopkgs),
                              "library %s contains no packages",
                              "libraries %s contain no packages",
                              domain = "R-base"),
                     pkglist)
      warning(msg, domain=NA)
  }

  Note that it is much better to have complete clauses as here, since in another language one might need to say ‘There is no package in library %s’ or ‘There are no packages in libraries %s’.

1.8 Internationalization

There are mechanisms to translate the R- and C-level error and warning messages. There are only available if R is compiled with NLS support (which is requested by configure option --enable-nls, the default).

The procedures make use of msgfmt and xgettext which are part of GNU gettext and this will need to be installed: x86_64 Windows users can find pre-compiled binaries at https://www.stats.ox.ac.uk/pub/Rtools/goodies/gettext-tools.zip.

1.8.1 C-level messages

The process of enabling translations is

• In a header file that will be included in all the C (or C++ or Objective C/C++) files containing messages that should be translated, declare

  #include <R.h>  /* to include Rconfig.h */
  
  #ifdef ENABLE_NLS
  #include <libintl.h>
  #define _(String) dgettext ("pkg", String)
  /* replace pkg as appropriate */
  #else
  #define _(String) (String)
  #endif

• For each message that should be translated, wrap it in _(...), for example

  error(_("'ord' must be a positive integer"));

  If you want to use different messages for singular and plural forms, you need to add

  #ifndef ENABLE_NLS
  #define dngettext(pkg, String, StringP, N) (N == 1 ? String : StringP)
  #endif

  and mark strings by

  dngettext("pkg", <singular string>, <plural string>, n)

• In the package’s src directory run r xgettext --keyword=_ -o pkg.pot *.c

The file src/pkg.pot is the template file, and conventionally this is shipped as po/pkg.pot.

1.8.2 R messages

Mechanisms are also available to support the automatic translation of R stop, warning and message messages. They make use of message catalogs in the same way as C-level messages, but using domain R-pkg rather than pkg. Translation of character strings inside stop, warning and message calls is automatically enabled, as well as other messages enclosed in calls to gettext or gettextf. (To suppress this, use argument domain=NA.)

Tools to prepare the R-pkg.pot file are provided in package tools: xgettext2pot will prepare a file from all strings occurring inside gettext/gettextf, stop, warning and message calls. Some of these are likely to be spurious and so the file is likely to need manual editing. xgettext extracts the actual calls and so is more useful when tidying up error messages.

The R function ngettext provides an interface to the C function of the same name: see example in the previous section. It is safest to use domain="R-pkg" explicitly in calls to ngettext, and necessary for earlier versions of R unless they are calls directly from a function in the package.

1.8.3 Preparing translations

Once the template files have been created, translations can be made. Conventional translations have file extension .po and are placed in the po subdirectory of the package with a name that is either ll.po or R-ll.po for translations of the C and R messages respectively to language with code ll.

See Localization of messages in R Installation and Administration for details of language codes.

There is an R function, update_pkg_po in package tools, to automate much of the maintenance of message translations. See its help for what it does in detail.

If this is called on a package with no existing translations, it creates the directory pkgdir/po, creates a template file of R messages, pkgdir/po/R-pkg.pot, within it, creates the en@quot translation and installs that. (The en@quot pseudo-language interprets quotes in their directional forms in suitable (e.g. UTF-8) locales.)

If the package has C source files in its src directory that are marked for translation, use

touch pkgdir/po/pkg.pot

to create a dummy template file, then call update_pkg_po again (this can also be done before it is called for the first time).

When translations to new languages are added in the pkgdir/po directory, running the same command will check and then install the translations.

If the package sources are updated, the same command will update the template files, merge the changes into the translation .po files and then installed the updated translations. You will often see that merging marks translations as ‘fuzzy’ and this is reported in the coverage statistics. As fuzzy translations are not used, this is an indication that the translation files need human attention.

The merged translations are run through tools::checkPofile to check that C-style formats are used correctly: if not the mismatches are reported and the broken translations are not installed.

This function needs the GNU gettext-tools installed and on the path: see its help page.

1.9 CITATION files

An installed file named CITATION will be used by the citation() function. (It should be in the inst subdirectory of the package sources.)

The CITATION file is parsed as R code (in the package’s declared encoding, or in ASCII if none is declared). It will contain calls to function bibentry. Here is that for nlme:

## R package reference generated from DESCRIPTION metadata
citation(auto = meta)

## NLME book
bibentry(bibtype = "Book",
         title = "Mixed-Effects Models in S and S-PLUS",
         author = c(person(c("José", "C."), "Pinheiro"),
                    person(c("Douglas", "M."), "Bates")),
         year = "2000", publisher = "Springer", address = "New York",
         doi = "10.1007/b98882")

Note how the first call auto-generates citation information from object meta, a parsed version of the DESCRIPTION file – it is tempting to hardcode such information, but it normally then gets outdated. How the first entry would look like as a bibentry call can be seen from print(citation("pkgname", auto = TRUE), style = "R") for any installed package. Auto-generated information is returned by default if no CITATION file is present.

See ?bibentry for further details of the information which can be provided. In case a bibentry contains LaTeX markup (e.g., for accented characters or mathematical symbols), it may be necessary to provide a text representation to be used for printing via the textVersion argument to bibentry. E.g., earlier versions of nlme additionally used something like

         textVersion =
         paste0("Jose Pinheiro, Douglas Bates, Saikat DebRoy, ",
                "Deepayan Sarkar and the R Core Team (",
                sub("-.*", "", meta$Date),
                "). nlme: Linear and Nonlinear Mixed Effects Models. ",
                sprintf("R package version %s", meta$Version), ".")

The CITATION file should itself produce no output when source-d.

It is desirable (and essential for CRAN) that the CITATION file does not contain calls to functions such as packageDescription which assume the package is installed in a library tree on the package search path.

1.10 Package types

The DESCRIPTION file has an optional field Type which if missing is assumed to be Package, the sort of extension discussed so far in this chapter. Currently one other type is recognized; there used also to be a Translation type.

1.10.1 Frontend

This is a rather general mechanism, designed for adding new front-ends such as the former gnomeGUI package (see the Archive area on CRAN). If a configure file is found in the top-level directory of the package it is executed, and then if a Makefile is found (often generated by configure), make is called. If R CMD INSTALL --clean is used make clean is called. No other action is taken.

R CMD build can package up this type of extension, but R CMD check will check the type and skip it.

Many packages of this type need write permission for the R installation directory.

1.11 Services

Several members of the R project have set up services to assist those writing R packages, particularly those intended for public distribution.

win-builder.r-project.org offers the automated preparation of (x86_64) Windows binaries from well-tested source packages.

R-Forge (R-Forge.r-project.org) and RForge (www.rforge.net) are similar services with similar names. Both provide source-code management through SVN, daily building and checking, mailing lists and a repository that can be accessed via install.packages (they can be selected by setRepositories and the GUI menus that use it). Package developers have the opportunity to present their work on the basis of project websites or news announcements. Mailing lists, forums or wikis provide useRs with convenient instruments for discussions and for exchanging information between developers and/or interested useRs.

Footnotes