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Package: robCompositions Type: Package Title: Robust Estimation for Compositional Data Version: 2.0.8 Date: 2018-05-29 Depends: R (>= 3.0.0), robustbase, ggplot2, data.table, e1071, pls LinkingTo: Rcpp Imports: car, rrcov, cluster, cvTools, fpc, GGally, kernlab, MASS, mclust, Rcpp, sROC, VIM, zCompositions Suggests: knitr VignetteBuilder: knitr Authors@R: c( person("Matthias", "Templ", email="firstname.lastname@example.org", role=c("aut", "cre")), person("Karel", "Hron", email="Alexander.Kowarik@statistik.gv.at", role="aut"), person("Peter", "Filzmoser", email = "Bernhard.Meindl@statistik.gv.at", role="aut"), person("Petra", "Kynclova", email="email@example.com", role="ctb"), person("Jan", "Walach", email ="firstname.lastname@example.org ", role="ctb"), person("Veronika", "Pintar", email ="email@example.com", role="ctb"), person("Jiajia", "Chen", email ="firstname.lastname@example.org", role="ctb")) Maintainer: Matthias Templ <email@example.com> Description: 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. License: GPL (>= 2) LazyLoad: yes LazyData: true RoxygenNote: 6.0.1 NeedsCompilation: yes Packaged: 2018-05-29 10:31:32 UTC; teml Author: Matthias Templ [aut, cre], Karel Hron [aut], Peter Filzmoser [aut], Petra Kynclova [ctb], Jan Walach [ctb], Veronika Pintar [ctb], Jiajia Chen [ctb] Repository: CRAN Date/Publication: 2018-05-29 11:38:10 UTC