{% set version = "3.14.0" %}
{% 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: f87bb73d8bfee36c6bf4f15779c59ff6b70c70ca25b1388b4ee236757276d605

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-2.0-or-later
  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
