Sawitzki G. Computational Statistics: An Introduction to R

Boca Raton: CRC Press, 2009. - 251 p.Basic data analysis
R programming conventions
Generation of random numbers and patterns
Random numbers
Patterns
Case study: distribution diagnostics
Distribution functions
Histograms
Barcharts
Statistics of distribution functions; KolmogorovSmirnov tests
Monte Carlo confidence bands
Statistics of histograms and related plots; X2tests
Moments and quantiles
R complements
Random numbers
Graphical comparisons
Functions
Enhancing graphical displays
R internals
parse
eval
print
Executing files
Packages
Statistical summary
Literature and additional referencesRegression
General regression model
Linear model
Factors
Least squares estimation
Regression diagnostics
More examples for linear models
Model formulae
GaussMarkov estimator and residuals
Variance decomposition and analysis of variance
Simultaneous inference
Scheff́e's confidence bands
Tukey's confidence intervals
Case study: titre plates
Beyond linear regression
Transformations
Generalised linear models
Local regressionR complements
Discretisation
External data
Testing software
R data types
Classes and polymorphic functions
Extractor functions
Statistical summary
Literature and additional references. 3 Comparisons
Shift/scale families, and stochastic order
QQ plot, PP plot, and comparison of distributions
KolmogorovSmirnov tests
Tests for shift alternatives
Road map
Power and confidence
Theoretical power and confidence
Simulated power and confidence
Quantile estimation
Qualitative features of distributions
Statistical summary
Literature and additional references
Dimensions 1, 2, 3 ., c
R Complements
Dimensions
Selections
Projections
Marginal distributions and scatter plot matrices
Projection pursuit
Projections for dimensions 1, 2, 3 . 7
Parallel coordinates
Sections, conditional distributions and coplots
Transformations and dimension reduction
Higher dimensions
Linear case
Partial residuals and added variable plots
Nonlinear case
Example: cusp nonlinearity
Case study: Melbourne temperature data
Curse of dimensionality
Case study: body fat
High dimensions
Statistical summary
R as a programming language and environment
Help and information
Names and search paths
Administration and customisation
Basic data types
Output for objects
Object inspection
System inspection
Complex data types
Accessing components
Data manipulation
Operators
Functions
Debugging and profiling
Control structures
Input and output to data streams; external data
Libraries, packages
Mathematical operators and functions; linear algebra
Model descriptions
Graphic functions
Highlevel graphics
Lowlevel graphics
Annotations and legends
Graphic parameters and Llyout
Elementary statistical functions
Distributions, random numbers, densities.
Computing on the language.