# This file created by conda-build 3.21.4
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Mon Feb 22 16:22:50 2021
# ------------------------------------------------

package:
  name: r-mlr
  version: 2.19.0
source:
  sha256: 1149c9b453896481c85906045aa82d511d96979ddecbe5a3faf04f9f4a5e6113
  url:
    - https://cran.r-project.org/src/contrib/Archive/mlr/mlr_2.19.0.tar.gz
    - https://cran.r-project.org/src/contrib/mlr_2.19.0.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r36h6d2157b_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
    - libiconv 1.16 he774522_0
    - liblapack 3.9.0 8_mkl
    - libxml2 2.9.10 hf5bbc77_3
    - 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 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-assertthat 0.2.1 r36h6115d3f_2
    - r-backports 1.2.1 r36h6d2157b_0
    - r-base 3.6.3 hddad469_6
    - r-bbmisc 1.11 r36hda5aaf8_1003
    - r-brio 1.1.1 r36h6d2157b_0
    - r-callr 3.5.1 r36h142f84f_0
    - r-checkmate 2.0.0 r36hda5aaf8_1
    - r-cli 2.3.0 r36hc72bb7e_0
    - r-colorspace 2.0_0 r36h6d2157b_0
    - r-crayon 1.4.1 r36hc72bb7e_0
    - r-data.table 1.14.0 r36h6d2157b_0
    - r-desc 1.2.0 r36h6115d3f_1003
    - r-diffobj 0.3.3 r36h6d2157b_0
    - r-digest 0.6.27 r36h096a1c2_0
    - r-ellipsis 0.3.1 r36hda5aaf8_0
    - r-evaluate 0.14 r36h6115d3f_2
    - r-fansi 0.4.2 r36h6d2157b_0
    - r-farver 2.0.3 r36h796a38f_1
    - r-fastmatch 1.1_0 r36hda5aaf8_1005
    - r-ggplot2 3.3.3 r36hc72bb7e_0
    - r-glue 1.4.2 r36h6d2157b_0
    - r-gtable 0.3.0 r36h6115d3f_3
    - r-isoband 0.2.3 r36ha856d6a_0
    - r-jsonlite 1.7.2 r36h6d2157b_0
    - r-labeling 0.4.2 r36h142f84f_0
    - r-lattice 0.20_41 r36h6d2157b_3
    - r-lifecycle 1.0.0 r36hc72bb7e_0
    - r-magrittr 2.0.1 r36h6d2157b_1
    - r-mass 7.3_53.1 r36h6d2157b_0
    - r-matrix 1.3_2 r36hd2e18ed_0
    - r-mgcv 1.8_34 r36hd2e18ed_0
    - r-munsell 0.5.0 r36h6115d3f_1003
    - r-nlme 3.1_152 r36he816bda_0
    - r-parallelmap 1.5.0 r36h6115d3f_1
    - r-paramhelpers 1.14 r36hda5aaf8_1
    - r-pillar 1.4.7 r36hc72bb7e_0
    - r-pkgbuild 1.2.0 r36hc72bb7e_0
    - r-pkgconfig 2.0.3 r36h6115d3f_1
    - r-pkgload 1.1.0 r36h796a38f_0
    - r-praise 1.0.0 r36h6115d3f_1004
    - r-prettyunits 1.1.1 r36h6115d3f_1
    - r-processx 3.4.5 r36h6d2157b_0
    - r-ps 1.5.0 r36h6d2157b_0
    - r-r6 2.5.0 r36hc72bb7e_0
    - r-rcolorbrewer 1.1_2 r36h6115d3f_1003
    - r-rcpp 1.0.6 r36ha856d6a_0
    - r-rematch2 2.1.2 r36h6115d3f_1
    - r-rlang 0.4.10 r36h6d2157b_0
    - r-rprojroot 2.0.2 r36hc72bb7e_0
    - r-rstudioapi 0.13 r36hc72bb7e_0
    - r-scales 1.1.1 r36h6115d3f_0
    - r-stringi 1.5.3 r36ha856d6a_1
    - r-survival 3.2_7 r36h6d2157b_0
    - r-testthat 3.0.2 r36ha856d6a_0
    - r-tibble 3.0.6 r36h6d2157b_0
    - r-utf8 1.1.4 r36hda5aaf8_1003
    - r-vctrs 0.3.6 r36h6d2157b_0
    - r-viridislite 0.3.0 r36h6115d3f_1003
    - r-waldo 0.2.4 r36hc72bb7e_0
    - r-withr 2.4.1 r36hc72bb7e_0
    - r-xml 3.99_0.3 r36hda5aaf8_1
    - r-zeallot 0.1.0 r36h6115d3f_1002
    - vc 14.2 hb210afc_3
    - vs2015_runtime 14.28.29325 h5e1d092_3
    - zlib 1.2.11 h62dcd97_1010
  run:
    - m2w64-gcc-libs
    - r-backports >=1.1.0
    - r-base >=3.6,<3.7.0a0
    - r-bbmisc >=1.11
    - r-checkmate >=1.8.2
    - r-data.table >=1.12.4
    - r-ggplot2
    - r-parallelmap >=1.3
    - r-paramhelpers >=1.10
    - r-stringi
    - r-survival
    - r-xml
test:
  commands:
    - '"%R%" -e "library(''mlr'')"'
about:
  home: https://github.com/mlr-org/mlr
  license: BSD-2-Clause
  license_family: BSD
  license_file:
    - D:\bld\r-mlr_1614011190175\_h_env/lib/R/share/licenses/BSD_2_clause
    - LICENSE
  summary: Interface to a large number of classification and regression techniques,
    including machine-readable parameter descriptions. There is also an experimental
    extension for survival analysis, clustering and general, example-specific cost-sensitive
    learning. Generic resampling, including cross-validation, bootstrapping and subsampling.
    Hyperparameter tuning with modern optimization techniques, for single- and multi-objective
    problems. Filter and wrapper methods for feature selection. Extension of basic
    learners with additional operations common in machine learning, also allowing
    for easy nested resampling. Most operations can be parallelized.
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - conda-forge/r
