{% set version = "3.8.0" %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/mice_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/mice/mice_{{ version }}.tar.gz
  sha256: 04bc18d6cf225d626d4a5d52dd98a30a19662ae14263c83b51744efce25e7ec5

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]
  host:
    - r-base
    - r-mass
    - r-rcpp
    - r-broom
    - r-dplyr
    - r-lattice
    - r-mitml
    - r-nnet
    - r-rlang
    - r-rpart
    - r-survival
  run:
    - r-base
    - {{native}}gcc-libs         # [win]
    - r-mass
    - r-rcpp
    - r-broom
    - r-dplyr
    - r-lattice
    - r-mitml
    - r-nnet
    - r-rlang
    - r-rpart
    - r-survival

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

about:
  home: http://stefvanbuuren.github.io/mice/ , http://www.stefvanbuuren.name , http://www.stefvanbuuren.name/fimd/
  license: GPL-2 | GPL-3
  summary: Multiple imputation using Fully Conditional Specification (FCS) implemented by the
    MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>.
    Each variable has its own imputation model. Built-in imputation models are provided
    for continuous data (predictive mean matching, normal), binary data (logistic regression),
    unordered categorical data (polytomous logistic regression) and ordered categorical
    data (proportional odds). MICE can also impute continuous two-level data (normal
    model, pan, second-level variables). Passive imputation can be used to maintain
    consistency between variables. Various diagnostic plots are available to inspect
    the quality of the imputations.
  license_family: GPL3
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-3'

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

# Package: mice
# Type: Package
# Version: 3.5.0
# Title: Multivariate Imputation by Chained Equations
# Date: 2019-05-13
# Authors@R: c(person("Stef", "van Buuren", role = c("aut","cre"), email = "stef.vanbuuren@tno.nl"), person("Karin", "Groothuis-Oudshoorn", role = "aut", email = "c.g.m.oudshoorn@utwente.nl"), person("Alexander", "Robitzsch", role = "ctb", email = "robitzsch@ipn.uni-kiel.de"), person("Gerko","Vink", role = "ctb", email = "g.vink@uu.nl"), person("Lisa","Doove", role = "ctb", email = "lisa.doove@ppw.kuleuven.be"), person("Shahab","Jolani", role = "ctb", email = "s.jolani@maastrichtuniversity.nl"), person("Rianne","Schouten", role = "ctb", email = "R.M.Schouten@uu.nl"), person("Philipp","Gaffert", role = "ctb", email = "philipp.gaffert@gfk.com"), person("Florian","Meinfelder", role = "ctb", email = "florian.meinfelder@uni-bamberg.de"), person("Bernie","Gray", role = "ctb", email = "bfgray3@gmail.com"))
# Maintainer: Stef van Buuren <stef.vanbuuren@tno.nl>
# Depends: methods, R (>= 2.10.0), lattice
# Imports: broom, dplyr, grDevices, graphics, MASS, mitml, nnet, parallel, Rcpp, rlang, rpart, splines, stats, survival, utils
# Suggests: AGD, CALIBERrfimpute, DPpackage, gamlss, lme4, mitools, nlme, pan, randomForest, Zelig, BSDA, knitr, rmarkdown, testthat, HSAUR3, micemd, miceadds, tidyr
# LinkingTo: Rcpp
# Description: Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
# Encoding: UTF-8
# License: GPL-2 | GPL-3
# LazyLoad: yes
# LazyData: yes
# URL: http://stefvanbuuren.github.io/mice/ , http://www.stefvanbuuren.name , http://www.stefvanbuuren.name/fimd/
# BugReports: https://github.com/stefvanbuuren/mice/issues
# RoxygenNote: 6.1.1
# NeedsCompilation: yes
# Packaged: 2019-05-13 20:40:26 UTC; buurensv
# Author: Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Alexander Robitzsch [ctb], Gerko Vink [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Rianne Schouten [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb]
# Repository: CRAN
# Date/Publication: 2019-05-13 21:20:20 UTC
