{% set version = "2.3.1" %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

package:
  name: r-robcompositions
  version: {{ version|replace("-", "_") }}

source:
  url:
    - {{ cran_mirror }}/src/contrib/robCompositions_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/robCompositions/robCompositions_{{ version }}.tar.gz
  sha256: 0b952857182e1d1862ee0b6bbcbd4cfd96eda1ccd4900987cdda6856451ea08e

build:
  merge_build_host: true  # [win]
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - {{ compiler('c') }}              # [not win]
    - {{ compiler('m2w64_c') }}        # [win]
    - {{ compiler('cxx') }}            # [not win]
    - {{ compiler('m2w64_cxx') }}      # [win]
    - {{ posix }}filesystem        # [win]
    - {{ posix }}make
    - {{ posix }}sed               # [win]
    - {{ posix }}coreutils         # [win]
    - {{ posix }}zip               # [win]
    - cross-r-base {{ r_base }}    # [build_platform != target_platform]
  host:
    - r-base
    - r-ggally
    - r-mass
    - r-rcpp
    - r-rcppeigen
    - r-vim
    - r-car
    - r-cluster
    - r-cvtools
    - r-data.table
    - r-e1071
    - r-fda
    - r-fpc
    - r-ggfortify
    - r-ggplot2
    - r-kernlab
    - r-mclust
    - r-pls
    - r-reshape2
    - r-robusthd
    - r-robustbase
    - r-rrcov
    - r-tidyr
    - r-zcompositions
  run:
    - r-base
    - {{ native }}gcc-libs         # [win]
    - r-ggally
    - r-mass
    - r-rcpp
    - r-rcppeigen
    - r-vim
    - r-car
    - r-cluster
    - r-cvtools
    - r-data.table
    - r-e1071
    - r-fda
    - r-fpc
    - r-ggfortify
    - r-ggplot2
    - r-kernlab
    - r-mclust
    - r-pls
    - r-reshape2
    - r-robusthd
    - r-robustbase
    - r-rrcov
    - r-tidyr
    - r-zcompositions

test:
  commands:
    - $R -e "library('robCompositions')"           # [not win]
    - "\"%R%\" -e \"library('robCompositions')\""  # [win]

about:
  home: https://CRAN.R-project.org/package=robCompositions
  license: GPL-2.0-or-later
  summary: 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_family: GPL2
  license_file:
    - {{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2

extra:
  recipe-maintainers:
    - conda-forge/r

# Package: robCompositions
# Type: Package
# Title: Compositional Data Analysis
# Version: 2.2.1
# Date: 2020-02-10
# Depends: R (>= 3.5.0), 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="matthias.templ@gmail.com", role=c("aut", "cre"), comment=c(ORCID="0000-0002-8638-5276")), person("Karel", "Hron", email="Alexander.Kowarik@statistik.gv.at", role="aut", comment = c(ORCID="0000-0002-1847-6598")), person("Peter", "Filzmoser", email = "Bernhard.Meindl@statistik.gv.at", role="aut", comment = c(ORCID="0000-0002-8014-4682")), person("Kamila", "Facevicova", email="kamila.facevicova@gmail.com", role="ctb"), person("Petra", "Kynclova", email="kynclova.petra@gmail.com", role="ctb"), person("Jan", "Walach", email ="walach.jan@gmail.com ", role="ctb"), person("Veronika", "Pintar", email ="vroni.pintar@gmx.at", role="ctb"), person("Jiajia", "Chen", email ="chenjiajia0401@163.com", role="ctb"), person("Dominika", "Miksova", email ="miksovadominika1@gmail.com", role="ctb"), person("Bernhard", "Meindl", email ="bernhard.meindl@gmail.com", role="ctb"), person("Alessandra", "Menafoglio", email = "alessandra.menafoglio@polimi.it", role="ctb", comment = c(ORCID="0000-0003-0682-6412")), person("Alessia", "Di Blasi", email = "alessia.diblasi@mail.polimi.it", role="ctb"), person("Federico", "Pavone", email = "federico.pavone@mail.polimi.it", role="ctb"), person("Gianluca", "Zeni", email = "gianluca.zeni@mail.polimi.it", role="ctb"))
# Maintainer: Matthias Templ <matthias.templ@gmail.com>
# 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: 2020-02-10 11:10:50 UTC; matthias
# Author: Matthias Templ [aut, cre] (<https://orcid.org/0000-0002-8638-5276>), Karel Hron [aut] (<https://orcid.org/0000-0002-1847-6598>), Peter Filzmoser [aut] (<https://orcid.org/0000-0002-8014-4682>), Kamila Facevicova [ctb], Petra Kynclova [ctb], Jan Walach [ctb], Veronika Pintar [ctb], Jiajia Chen [ctb], Dominika Miksova [ctb], Bernhard Meindl [ctb], Alessandra Menafoglio [ctb] (<https://orcid.org/0000-0003-0682-6412>), Alessia Di Blasi [ctb], Federico Pavone [ctb], Gianluca Zeni [ctb]
# Repository: CRAN
# Date/Publication: 2020-02-11 17:20:02 UTC
