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Tip revision: 9d594684d2bd4630b4501eac818563d0509628d4 authored by Matthias Templ on 14 August 2017, 12:14:11 UTC
version 2.0.6
Tip revision: 9d59468
Package: robCompositions
Type: Package
Title: Robust Estimation for Compositional Data
Version: 2.0.6
Date: 2017-08-14
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
Suggests: knitr
VignetteBuilder: knitr
Author: Matthias Templ, Karel Hron, Peter Filzmoser
Maintainer: Matthias Templ <>
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: 2017-08-14 12:03:28 UTC; teml
Repository: CRAN
Date/Publication: 2017-08-14 13:14:11 UTC
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