# This file created by conda-build 3.18.11
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Sat Feb 22 01:46:42 2020
# ------------------------------------------------

package:
    name: r-mice
    version: 3.8.0
source:
    sha256: 04bc18d6cf225d626d4a5d52dd98a30a19662ae14263c83b51744efce25e7ec5
    url:
        - https://cran.r-project.org/src/contrib/Archive/mice/mice_3.8.0.tar.gz
        - https://cran.r-project.org/src/contrib/mice_3.8.0.tar.gz
build:
    merge_build_host: true
    number: '0'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r35h796a38f_0
requirements:
    build:
        - m2-bash 4.3.042 5
        - m2-coreutils 8.25 102
        - m2-filesystem 2016.04 4
        - m2-gcc-libs 5.3.0 4
        - m2-gmp 6.1.0 3
        - m2-libbz2 1.0.6 3
        - m2-libiconv 1.14 3
        - m2-libintl 0.19.7 4
        - m2-make 4.1 5
        - m2-msys2-runtime 2.5.0.17080.65c939c 3
        - m2-sed 4.2.2 3
        - m2-zip 3.0 2
        - m2w64-binutils 2.25.1 5
        - m2w64-bzip2 1.0.6 6
        - m2w64-crt-git 5.0.0.4636.2595836 2
        - m2w64-gcc 5.3.0 6
        - m2w64-gcc-ada 5.3.0 6
        - m2w64-gcc-fortran 5.3.0 6
        - m2w64-gcc-libgfortran 5.3.0 6
        - m2w64-gcc-libs 5.3.0 7
        - m2w64-gcc-libs-core 5.3.0 7
        - m2w64-gcc-objc 5.3.0 6
        - m2w64-gmp 6.1.0 2
        - m2w64-headers-git 5.0.0.4636.c0ad18a 2
        - m2w64-isl 0.16.1 2
        - m2w64-libiconv 1.14 6
        - m2w64-libmangle-git 5.0.0.4509.2e5a9a2 2
        - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
        - m2w64-make 4.1.2351.a80a8b8 2
        - m2w64-mpc 1.0.3 3
        - m2w64-mpfr 3.1.4 4
        - m2w64-pkg-config 0.29.1 2
        - m2w64-toolchain 5.3.0 7
        - m2w64-toolchain_win-64 2.4.0 0
        - m2w64-tools-git 5.0.0.4592.90b8472 2
        - m2w64-windows-default-manifest 6.4 3
        - m2w64-winpthreads-git 5.0.0.4634.697f757 2
        - m2w64-zlib 1.2.8 10
        - msys2-conda-epoch 20160418 1
    host:
        - _r-mutex 1.0.1 anacondar_1
        - intel-openmp 2020.0 166
        - libblas 3.8.0 15_mkl
        - m2w64-bwidget 1.9.10 2
        - m2w64-bzip2 1.0.6 6
        - m2w64-expat 2.1.1 2
        - m2w64-fftw 3.3.4 6
        - m2w64-flac 1.3.1 3
        - m2w64-gcc-libgfortran 5.3.0 6
        - m2w64-gcc-libs 5.3.0 7
        - m2w64-gcc-libs-core 5.3.0 7
        - m2w64-gettext 0.19.7 2
        - m2w64-gmp 6.1.0 2
        - m2w64-gsl 2.1 2
        - m2w64-icu 58.2 heb44b8b_2
        - m2w64-libiconv 1.14 6
        - m2w64-libjpeg-turbo 1.4.2 3
        - m2w64-libogg 1.3.2 3
        - m2w64-libpng 1.6.21 2
        - m2w64-libsndfile 1.0.26 2
        - m2w64-libtiff 4.0.6 2
        - m2w64-libvorbis 1.3.5 2
        - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
        - m2w64-libxml2 2.9.3 4
        - m2w64-mpfr 3.1.4 4
        - m2w64-nlopt 2.4.2 3
        - m2w64-openblas 0.2.19 1
        - m2w64-pcre 8.38 2
        - m2w64-speex 1.2rc2 3
        - m2w64-speexdsp 1.2rc3 3
        - m2w64-tcl 8.6.5 3
        - m2w64-tk 8.6.5 3
        - m2w64-tktable 2.10 5
        - m2w64-wineditline 2.101 5
        - m2w64-xz 5.2.2 2
        - m2w64-zlib 1.2.8 10
        - mkl 2020.0 166
        - msys2-conda-epoch 20160418 1
        - r-assertthat 0.2.1 r35h6115d3f_1
        - r-backports 1.1.5 r35hda5aaf8_0
        - r-base 3.5.1 h0c8e037_1012
        - r-boot 1.3_24 r35h6115d3f_0
        - r-broom 0.5.4 r35h6115d3f_0
        - r-cli 2.0.1 r35h6115d3f_0
        - r-clipr 0.7.0 r35h6115d3f_0
        - r-crayon 1.3.4 r35h6115d3f_1002
        - r-digest 0.6.24 r35h796a38f_0
        - r-dplyr 0.8.4 r35h796a38f_0
        - r-ellipsis 0.3.0 r35hda5aaf8_0
        - r-fansi 0.4.1 r35hda5aaf8_0
        - r-forcats 0.4.0 r35h6115d3f_1
        - r-generics 0.0.2 r35h6115d3f_1002
        - r-glue 1.3.1 r35hda5aaf8_1
        - r-haven 2.1.1 r35h796a38f_1
        - r-hms 0.5.3 r35h6115d3f_0
        - r-jomo 2.6_10 r35hda5aaf8_0
        - r-lattice 0.20_40 r35hda5aaf8_0
        - r-lifecycle 0.1.0 r35h6115d3f_0
        - r-lme4 1.1_21 r35h796a38f_1
        - r-magrittr 1.5 r35h6115d3f_1002
        - r-mass 7.3_51.5 r35hda5aaf8_0
        - r-matrix 1.2_18 r35hda5aaf8_1
        - r-minqa 1.2.4 r35h5b3a9a7_1004
        - r-mitml 0.3_7 r35h6115d3f_1
        - r-nlme 3.1_144 r35h17ddedb_0
        - r-nloptr 1.2.1 r35h796a38f_1
        - r-nnet 7.3_12 r35hda5aaf8_1003
        - r-numderiv 2016.8_1.1 r35h6115d3f_1
        - r-ordinal 2019.12_10 r35hda5aaf8_0
        - r-pan 1.6 r35h17ddedb_2
        - r-pillar 1.4.3 r35h6115d3f_0
        - r-pkgconfig 2.0.3 r35h6115d3f_0
        - r-plogr 0.2.0 r35h6115d3f_1002
        - r-plyr 1.8.5 r35h796a38f_0
        - r-purrr 0.3.3 r35hda5aaf8_0
        - r-r6 2.4.1 r35h6115d3f_0
        - r-rcpp 1.0.3 r35h796a38f_0
        - r-rcppeigen 0.3.3.7.0 r35h796a38f_0
        - r-readr 1.3.1 r35h796a38f_1002
        - r-reshape2 1.4.3 r35h796a38f_1004
        - r-rlang 0.4.4 r35hda5aaf8_0
        - r-rpart 4.1_15 r35hda5aaf8_1
        - r-stringi 1.4.6 r35h796a38f_0
        - r-stringr 1.4.0 r35h6115d3f_1
        - r-survival 3.1_8 r35hda5aaf8_0
        - r-tibble 2.1.3 r35hda5aaf8_1
        - r-tidyr 1.0.2 r35h796a38f_0
        - r-tidyselect 1.0.0 r35h6115d3f_0
        - r-ucminf 1.1_4 r35h17ddedb_1004
        - r-utf8 1.1.4 r35hda5aaf8_1001
        - r-vctrs 0.2.3 r35hda5aaf8_0
        - r-zeallot 0.1.0 r35h6115d3f_1001
    run:
        - m2w64-gcc-libs
        - r-base >=3.5,<3.6.0a0
        - r-broom
        - r-dplyr
        - r-lattice
        - r-mass
        - r-mitml
        - r-nnet
        - r-rcpp
        - r-rlang
        - r-rpart
        - r-survival
test:
    commands:
        - '"%R%" -e "library(''mice'')"'
about:
    home: http://stefvanbuuren.github.io/mice/ , http://www.stefvanbuuren.name , http://www.stefvanbuuren.name/fimd/
    license: GPL-2 | GPL-3
    license_family: GPL3
    license_file: D:\bld\r-mice_1582336187442\_h_env/lib/R/share/licenses/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.
extra:
    copy_test_source_files: true
    final: true
    recipe-maintainers:
        - conda-forge/r
        - slacalle
