PROGRAMS
Peter Filzmoser

Robust joint modeling of mean and dispersion through trimming:

(N.M. Neykov, P. Filzmoser, and P.N. Neytchev)
R code and information

Imputation of missing values for compositional data using classical and robust methods:

(K. Hron, M. Templ, and P. Filzmoser)
robCompositions_1.0.tar.gz R-package `robCompositions' (tar.gz file for Linux, Mac) for running the procedures according to the paper (preprint).

A context-sensitive method for robust model selection with application to analyzing success factors of communities:

(A. Alfons, W.E. Baaske, P. Filzmoser, W. Mader, and R. Wieser)
csselect.R R-program for robust model selection according to the paper (preprint). The program B-RLARS.R is also needed.

Robust factor analysis for compositional data:

(together with K. Hron, C. Reimann, R.G. Garrett)
CAGeo08Figs.R R-scripts for generating all figures in the paper. One also needs the programs pfa1.R and factanal.fit.principal1.R .

Outlier detection for compositional data using robust methods:

(together with K. Hron)
progsOutComp.R R-scripts for generating all figures in the paper. One also needs the programs alr.R , invalr.R , iso_new.R , inviso_new.R , and drawMahal.R .

twoway.rob

(together with Christophe Croux, Belgium)
Program for the robust additive and multiplicative fit of two-way tables.
The program is written in SPlus, and it needs the functions prcomp.rob (robust principal component analysis) and weight.wl1 (calculating the weights for weighted L1-regression). For drawing robust biplots of a "twoway.rob"-object, the function twoway.bip can be used.

factanal.ccr

(together with C. Croux, G. Pison, P.J. Rousseeuw; Belgium)
Program for "Fitting Multiplicative Models by Robust Alternating Regression".
The program is written in SPlus, and it needs the functions prcomp.rob (robust principal component analysis), weight.wl1 (calculating the weights for weighted L1-regression), and ccrfit (robust fit of twoway-tables). For drawing screeplots, the functions screeplot.faccr or screeplot.fanova can be used.

Robust canonical correlation analysis (CCA):

(together with J. Branco, C. Croux, R. Oliveira)
cc.ssc Splus program for CCA based on a robust covariance matrix estimation; uses mesthub (M estimator)
pp.ssc Splus program for robust CCA based on projection pursuit
cancor.rar R program for CCA based on robust alternating regressions
simcc.ssc Splus program for doing simulations