R packages and functions for multivariate analysis

package::function #comments

• Visualization of multivariate data 

graphics::pairs(), stars(), mosaicplot()
graphics::coplot() #conditioning plot
lattice::xyplot(), splom()
car::scatterplot.matrix()
scatterplot3d::scatterplot3d()
aplpack::spin3R(), faces()
MASS::parcoord
ade4::mstree()
vegan::spantree()
ellipse::plotcorr()
vcd::mosaic()
gclus:: #cluster specific graphical enhancements for scatter plots
xgobi::
rggobi::

•  Hypothesis testing

ICSNP:: #HotellingsT2 test, non-parametric
cramer::
SpatialNP::

•  Multivariate distributions

stats::cov(), cor()
INSNP::spatial.median()
MASS::cov.rob()
covRobust::
robustbase::covMCD(), covOGK()
rrcov::
mvtnorm:: #simulation
mnormt::
sn:: #for skew normal and t
ks::rmvnorm.mixt(), dmvnorm.mixt() #comprehensive information on mixtures;
bayesm::rwishart() #Wishart distribution
MCMCpack::rwish()

#multivariate normality test
MVN::HZ.test() #Henze-Zirkler’s Multivariate Normality Test
MVN::mardia.test() #Mardia’s Multivariate Normality Test
MVN::royston.test() #Royston’s Multivariate Normality Test
mvnormtest::mshapiro.test() #Shapiro-Wilk multivariate normality Test
mvoutlier::
energy::mvnorm.etest(), k.sample()
stats::mauchly.test

#Copulas
copula:: #generalized archimedian copula

•  Linear models

stats::lm() #wiht matrix specified as dependent variable.
stats::anova.mlm(), manova()
PenLNM:: #penalized logistic normal multinomial regression.
sn::msn.mle(), mst.mle() #fit multivariate skew normal and skew t model.
pls:: #partial least squares regression, principle component regression
ppls:: #panelized partial least squares.
dr:: #dimension reduction regression, options: "sir", "save"
plsgenomices::
relaimpo: #relative importance of regression parameters

• Projection methods

#principal components
stats::prcomp() based on svd(), princomp() based on eigen(). #the former is preferred.
sca::
Hmisc::pc1()
paran:: #Horn's evaluation of the number of dimensions to retain
pcurve:: #principle curve analysis/visualization
gmodels::fast.prcomp(), fast.svd #wide matrices
kernlab::kpca #non-linear principle components.
pcaPP::acpgen(), acprob()
psy::sphpca() #maps into a sphere, fpca(), scree.plot() #some variables as dependent
#Canonical correlation:
stats::cancor()
kernlab::kcca()
corcor::
#Redundancey analysis
calibrate::rda()
fso::
stats::cmdscale()
SensoMineR::MDS.indscal()

• Classification

#unsupervised
Cluster::
kmeans::hclust()
cluster::
clv::
trimcluster::
clue::
clusterSim::
hybridHclust::
energy::edist(), hclust.energy()
kohonen::
clusterGeneration::
mclust::
MachineLearning:: #tree
rpart::
TWIX::
mvpart::
party::
caret:: #classification and regression training
kknn:: #k-nearest

#supervised
MASS::lda(), qda()
mda::mda() #mixture and flexible discriminant analysis
mars::fda() #multivariate adaptive regression splines. bruto() #adaptive spline backfitting
earth::
rda::
class:: knn()
SensoMineR::FDA()
klaR:: #variable selection and robustness against multicollinearity and visualization
superpc:: #supervised pca
hddplot:: #cross-validated linear discriminant.
ROCR:: #assessing classifier performance

• Corresponding analysis

MASS::mca(), corresp()
ca::ca()
ade4::mca(), hta()
FactoMineR::CA(), MCA()
homals::

• Modeling non-Gaussian data

MNP::
polycor::
bayesm::
VGAM::

• Matrix manipulations

Matrix::
SparseM::
matrixcalc:: matrix differential calculus.
spam::