Revision 775c7e1be4c58aaf8adccdd2b92d07aa9cdc265f authored by Matthias Templ on 14 January 2020, 05:10 UTC, committed by cran-robot on 14 January 2020, 05:10 UTC
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Package: robCompositions
Type: Package
Title: Compositional Data Analysis
Version: 2.2.0
Date: 2020-01-13
Depends: R (>= 3.0.0), ggplot2, pls, data.table
LinkingTo: Rcpp, RcppEigen
Imports: car, cvTools, e1071, rrcov, cluster, fpc, GGally, kernlab,
        MASS, mclust, sROC, tidyr, robustbase, VIM, zCompositions,
        reshape2, Rcpp
Suggests: knitr, testthat
VignetteBuilder: knitr
Authors@R: c(
  person("Matthias", "Templ", email="", role=c("aut", "cre"), comment=c(ORCID="0000-0002-8638-5276")),
  person("Karel", "Hron", email="", role="aut"),
  person("Peter", "Filzmoser", email = "", role="aut"),
  person("Kamila", "Facevicova", email="", role="ctb"),
  person("Petra", "Kynclova", email="", role="ctb"),
  person("Jan", "Walach", email =" ", role="ctb"),
  person("Veronika", "Pintar", email ="", role="ctb"),
  person("Jiajia", "Chen", email ="", role="ctb"),	
  person("Dominika", "Miksova", email ="", role="ctb"),
  person("Bernhard", "Meindl", email ="", role="ctb"),
  person("Alessia", "Di Blasi", email = "", role="ctb"),
  person("Federico", "Pavone", email = "", role="ctb"),
  person("Gianluca", "Zeni", email = "", role="ctb"))
Maintainer: Matthias Templ <>
Description: Methods for analysis of compositional data including robust
    methods  (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace 
    rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), 
    count zeros (<doi:10.1177/1471082X14535524>), 
    methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (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, functional data analysis and p-splines (<doi:10.1016/j.csda.2015.07.007>), 
    contingency (<doi:10.1080/03610926.2013.824980>) 
    and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) 
    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
Encoding: UTF-8
RoxygenNote: 7.0.0
NeedsCompilation: yes
Packaged: 2020-01-13 05:50:40 UTC; matthias
Author: Matthias Templ [aut, cre] (<>),
  Karel Hron [aut],
  Peter Filzmoser [aut],
  Kamila Facevicova [ctb],
  Petra Kynclova [ctb],
  Jan Walach [ctb],
  Veronika Pintar [ctb],
  Jiajia Chen [ctb],
  Dominika Miksova [ctb],
  Bernhard Meindl [ctb],
  Alessia Di Blasi [ctb],
  Federico Pavone [ctb],
  Gianluca Zeni [ctb]
Repository: CRAN
Date/Publication: 2020-01-14 06:10:03 UTC
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