Concept index

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	Index Entry		Section
A
	Accessing builtin datasets:		Accessing builtin datasets
	Additive models:		Some non-standard models
	Analysis of variance:		Analysis of variance and model comparison
	Arithmetic functions and operators:		Vector arithmetic
	Arrays:		Arrays
	Assignment:		Vectors and assignment
	Attributes:		Objects
B
	Binary operators:		Defining new binary operators
	Box plots:		One- and two-sample tests
C
	Character vectors:		Character vectors
	Classes:		The class of an object
	Classes:		Object orientation
	Concatenating lists:		Concatenating lists
	Contrasts:		Contrasts
	Control statements:		Control statements
	CRAN:		Contributed packages and CRAN
	Customizing the environment:		Customizing the environment
D
	Data frames:		Data frames
	Default values:		Named arguments and defaults
	Density estimation:		Examining the distribution of a set of data
	Determinants:		Singular value decomposition and determinants
	Diverting input and output:		Executing commands from or diverting output to a file
	Dynamic graphics:		Dynamic graphics
E
	Eigenvalues and eigenvectors:		Eigenvalues and eigenvectors
	Empirical CDFs:		Examining the distribution of a set of data
F
	Factors:		Factors
	Factors:		Contrasts
	Families:		Families
	Formulae:		Formulae for statistical models
G
	Generalized linear models:		Generalized linear models
	Generalized transpose of an array:		Generalized transpose of an array
	Generic functions:		Object orientation
	Graphics device drivers:		Device drivers
	Graphics parameters:		The par() function
	Grouped expressions:		Grouped expressions
I
	Indexing of and by arrays:		Array indexing
	Indexing vectors:		Index vectors
K
	Kolmogorov-Smirnov test:		Examining the distribution of a set of data
L
	Least squares fitting:		Least squares fitting and the QR decomposition
	Linear equations:		Linear equations and inversion
	Linear models:		Linear models
	Lists:		Lists
	Local approximating regressions:		Some non-standard models
	Loops and conditional execution:		Loops and conditional execution
M
	Matrices:		Arrays
	Matrix multiplication:		Multiplication
	Maximum likelihood:		Maximum likelihood
	Missing values:		Missing values
	Mixed models:		Some non-standard models
N
	Named arguments:		Named arguments and defaults
	Namespace:		Namespaces
	Nonlinear least squares:		Nonlinear least squares and maximum likelihood models
O
	Object orientation:		Object orientation
	Objects:		Objects
	One- and two-sample tests:		One- and two-sample tests
	Ordered factors:		Factors
	Ordered factors:		Contrasts
	Outer products of arrays:		The outer product of two arrays
P
	Packages:		R and statistics
	Packages:		Packages
	Probability distributions:		Probability distributions
Q
	QR decomposition:		Least squares fitting and the QR decomposition
	Quantile-quantile plots:		Examining the distribution of a set of data
R
	Reading data from files:		Reading data from files
	Recycling rule:		Vector arithmetic
	Recycling rule:		The recycling rule
	Regular sequences:		Generating regular sequences
	Removing objects:		Data permanency and removing objects
	Robust regression:		Some non-standard models
S
	Scope:		Scope
	Search path:		Managing the search path
	Shapiro-Wilk test:		Examining the distribution of a set of data
	Singular value decomposition:		Singular value decomposition and determinants
	Statistical models:		Statistical models in R
	Student’s t test:		One- and two-sample tests
T
	Tabulation:		Frequency tables from factors
	Tree-based models:		Some non-standard models
U
	Updating fitted models:		Updating fitted models
V
	Vectors:		Simple manipulations numbers and vectors
W
	Wilcoxon test:		One- and two-sample tests
	Workspace:		Data permanency and removing objects
	Writing functions:		Writing your own functions

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