
The R package
R,
closely related to the commercial package SPlus,
is the largest and most comprehensive public domain statistical
computing environment. It provides a coherent, flexible
programming environment for data analysis, applied mathematics,
statistical analysis, and graphics. Unlike some menudrived
statistical packages, the user interacts with R with a Clike command
language with popup graphical windows. The core R package is
enhanced by several hundred usersupplied addon packages in the Comprehensive R
Archive Network
(CRAN) and the Omegahat
Project
for Statistical Computing. Binary executables and
open source
codes for Linux, Windows and MacOS can be downloaded for instant
use. R has extensive documentation.
Here we list some of its capabilities that may be of interest to the
physical scientist.
The base R
package includes:
 arithmetic (scalar/vector/array)
 bootstrap resampling and confidence intervals (basic,
ABC,
percentile, studentized, tilted, jackknife)
 correlation coefficients (Pearson, Kendall, Spearman)
 distributions (Gaussian, Poisson, and many other
statistical distributions and special functions, including random
deviates)
 empirical distribution tests (AndersonDarling,
Cramervon
Mises, KolmogorovSmirnov) and quantiles
 exploratory data analysis
 generalized linear & generalized additive modelling
 graphics, publicationquality (scatter, dendrograms,
lattice,
etc)
 integration and interpolation
 linear algebra and equation solutions (extensive methods)
 linear mixedeffects modelling
 linear modelling (including nonlinear functions),
resistant
regression. robust Mestimators
 linear & quadratic programming (simplex, penalized
constraints)
 local and ridge regression (loess, variograms)
 maximum likelihood estimation (AIC, BIC)
 multivariate analysis (tabulations, ANOVA, discriminant,
factor,
principal components, Mahalanobis distances, MANOVA, principal
components)
 multivariate cluster analyses (agglomerative and divisive
clustering, dissimilarity matrix, fuzzy, knearest neighbor, kmeans
& mmedioid partitioning, monothetic, recursive partitioning,
regression trees, selforganizing maps)
 neural networks (censored, leastsquares, entropy,
loglinear,
maximum likelihood, perceptron)
 nonlinear leastsquares regression
 smoothing (crossvalidation, histograms, kernel, local
regression, variogram)
 sorting
 spatial analysis & point processes (correlogram,
kriging,
Moran's I, Geary's C, pattern analysis, polynomial surface, simulation,
variogram)
 splines (Bspline, periodic, polynomial)
 statistical tests, parametric & nonparametric
(Ansari,
Bartlett, binomial, Box, F, Fisher, Fligner, Friedman, MantelHaenzel,
Mauchley, McNemar, Mood, proportions, Shapiro, t, Wilcoxon, signed
rank),
 survival analysis for censored data (Cox regression,
KaplanMeier &
FlemingHarrington survival curves, life table, linear regression,
ridge regression, tobit modelling, Weibull & other survival curve
fitting, ksample tests)
 time series analysis (ARMA, acf, BoxJenkins, FFT, Kalman
filter, lags, mixedeffects, prediction, smoothing, spectral
analysis)
CRAN addon packages treat:
(see Chapter
5 for
brief individual descriptions)
 adaptive quadrature
 ARIMA modeling
 Bayesian computation (empirical Bayes, MCMC calculations
&
diagnostics, survival regression, logit/probit, networks
 Boolean hypotheses
 boosting
 bootstrap modelling
 classification and regression trees
 convex clustering & convex hulls
 conditional inference
 combinatorics
 elliptical confidence regions
 energy statistical tests
 extreme value distribution
 fixed point clusters
 genetic algorithms
 geostatistical modelling
 GUIs (Rcmdr, SciViews)
 heteroscedastic tregression
 hidden Markov models
 hierarchical partitioning & clustering
 independent component analysis
 interpolation
 irregular time series.
 kernel smoothing
 kernelbased machine learning
 knearest neighbor tree classifier
 KolmogorovZurbenko adaptive filtering
 leastangle and lasso regression
 linear programming (simplex)
 likelihood ratios
 local regression density estimators
 logistic regression
 map projections
 Matlab emulator
 matrices, sparse matrices, tensor decomposition
 Markov chain Monte Carlo
 mixture models
 mixture discriminant analysis
 modelbased clustering
 nonlinear least squares
 Markov multistate models
 mixture models & regression
 multidimensional analysis
 multimodality test
 multivariate time series
 multivariate ShapiroWilk test
 multivariate outlier detection
 multivariate normal partitioning
 multivariate normals with missing data
 neural networks
 nonlinear time series analysis
 nonparametric multiple comparisons
 omnibus tests for normality
 orientation data, outlier detection
 parallel coordinates plots
 partial least squares
 periodic autoregression analysis
 PoissonGamma additive models
 polychoric and polyserial correlations
 principal component regression
 principal curve fits
 projection pursuit
 proportional hazards modelling
 quantile regression
 quasivariances
 random fields
 random forest classification
 ridge regression
 robust regression
 Sampford sampling
 segmented regression break points
 selforganizing maps
 shape analysis
 spacetime ecological data analysis
 spatial analysis and kriging
 spline fits & regressions (MARS, BRUTO)
 structural regression with splines
 tesselations & Delaunay trangulation
 threedimensional visualization
 twostage least squares regression
 unit root tests
 variogram diagnostics
 wavelet toolbox & denoising
 weighted likelihood robust inference
CRAN
includes codes and datasets associated with textbooks on:
 Bayesian statistics
 bootstrapping
 circular statistics
 contingency tables
 data analysis
 engineering statistics
 econometrics
 kernel smoothing
 generalized additive models
 image analysis
 linear regression
 relative distribution methods
 smoothing
 survival analysis (censored data)
 timefrequency analysis
Through
base R,
CRAN and the Omegahat Project, R interfaces to the following languages,
formats and protocols:
 Languages :
BUGS, C,
Fortran,
Java, Python, Perl, XLisp
 Headers: XML
 I/O file structures: ASCII, binary,
bitmapped images, ftp, gzip, MIM, Oracle, SAS, SPlus, SPSS, Systat,
Stata, URL, .wav)
 Web formats : cgi, HTML, Netscape, SOAP
 Statistics
packages: GRASS, Matlab (emulator), XGobi
 Spreadsheets:
Excel,
Gnumeric
 Graphics: Grace,
Gtk, OpenGL, Tcl/Tk
 Databases: MySQL, SQL, SQLite
 Science/math
Libraries: GSL,
Isoda, LAPACK
 Parallel
processing:
PVM
 Text processors:
LaTeX
 Network connections: sockets, DCOM,
CORBA
