# This file created by conda-build 3.21.4
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Wed Mar  3 19:59:50 2021
# ------------------------------------------------

package:
  name: r-party
  version: '1.3_7'
source:
  sha256: f01c6164a3eb878a6a7ba90ac4e7b466d657c905f98985ccb48417f0f53f93df
  url:
    - https://cran.r-project.org/src/contrib/Archive/party/party_1.3-7.tar.gz
    - https://cran.r-project.org/src/contrib/party_1.3-7.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r36hcd874cb_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 2020.3 h57928b3_311
    - libblas 3.9.0 8_mkl
    - liblapack 3.9.0 8_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-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 3
    - m2w64-mpfr 3.1.4 4
    - 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 2020.4 hb70f87d_311
    - msys2-conda-epoch 20160418 1
    - r-base 3.6.3 hddad469_6
    - r-codetools 0.2_18 r36hc72bb7e_0
    - r-coin 1.4_1 r36h6d2157b_0
    - r-lattice 0.20_41 r36h6d2157b_3
    - r-libcoin 1.0_8 r36hd2e18ed_0
    - r-mass 7.3_53.1 r36h6d2157b_0
    - r-matrix 1.3_2 r36hd2e18ed_0
    - r-matrixstats 0.58.0 r36h6d2157b_0
    - r-modeltools 0.2_23 r36h6115d3f_1
    - r-multcomp 1.4_16 r36hc72bb7e_0
    - r-mvtnorm 1.1_1 r36h87ffe87_1
    - r-sandwich 3.0_0 r36h142f84f_0
    - r-strucchange 1.5_2 r36hda5aaf8_1
    - r-survival 3.2_7 r36h6d2157b_0
    - r-th.data 1.0_10 r36h6115d3f_2
    - r-zoo 1.8_8 r36hda5aaf8_0
  run:
    - m2w64-gcc-libs
    - r-base >=3.6,<3.7.0a0
    - r-coin >=1.1_0
    - r-modeltools >=0.2_21
    - r-mvtnorm >=1.0_2
    - r-sandwich >=1.1_1
    - r-strucchange
    - r-survival >=2.37_7
    - r-zoo
test:
  commands:
    - '"%R%" -e "library(''party'')"'
about:
  home: http://party.R-forge.R-project.org
  license: GPL-2.0-only
  license_family: GPL2
  license_file:
    - D:\bld\r-party_1614801931136\_h_env/lib/R/share/licenses/GPL-2
  summary: A computational toolbox for recursive partitioning. The core of the package
    is ctree(), an implementation of conditional inference trees which embed tree-structured  regression
    models into a well defined theory of conditional inference procedures. This non-parametric
    class of regression trees is applicable to all kinds of regression problems, including
    nominal, ordinal, numeric, censored as well as multivariate response variables
    and arbitrary measurement scales of the covariates.  Based on conditional inference
    trees, cforest() provides an implementation of Breiman's random forests. The function
    mob() implements an algorithm for recursive partitioning based on parametric models
    (e.g. linear models, GLMs or survival regression) employing parameter instability
    tests for split selection. Extensible functionality for visualizing tree-structured
    regression models is available. The methods are described in Hothorn et al. (2006)
    <doi:10.1198/106186006X133933>, Zeileis et al. (2008) <doi:10.1198/106186008X319331>
    and  Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - conda-forge/r
