robCompositions: Robust Estimation for Compositional Data

Methods for analysis of compositional data including robust methods, imputation, methods to replace rounded zeros, (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.

Version: 2.0.6
Depends: R (≥ 3.0.0), robustbase, ggplot2, data.table, e1071, pls
Imports: car, rrcov, cluster, cvTools, fpc, GGally, kernlab, MASS, mclust, Rcpp, sROC, VIM
LinkingTo: Rcpp
Suggests: knitr
Published: 2017-08-14
Author: Matthias Templ, Karel Hron, Peter Filzmoser
Maintainer: Matthias Templ <matthias.templ at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: robCompositions citation info
Materials: README NEWS
In views: OfficialStatistics
CRAN checks: robCompositions results


Reference manual: robCompositions.pdf
Vignettes: Imputation Methods in robCompositions
Overview of the robCompostions package
Package source: robCompositions_2.0.6.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: robCompositions_2.0.6.tgz
OS X Mavericks binaries: r-oldrel: robCompositions_2.0.6.tgz
Old sources: robCompositions archive

Reverse dependencies:

Reverse imports: mvoutlier
Reverse suggests: simFrame


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