# This file created by conda-build 3.19.2
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Wed May  6 14:19:46 2020
# ------------------------------------------------

package:
    name: r-logistf
    version: '1.23'
source:
    sha256: 5adb22a40569883395dc048c877f849dd08d07582a991f1b160f0338f0b13838
    url:
        - https://cran.r-project.org/src/contrib/Archive/logistf/logistf_1.23.tar.gz
        - https://cran.r-project.org/src/contrib/logistf_1.23.tar.gz
build:
    merge_build_host: true
    number: '2'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r40hda5aaf8_2
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
        - libiconv 1.15 hfa6e2cd_1006
        - liblapack 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-pcre2 10.34 0
        - 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 r40h6115d3f_2
        - r-backports 1.1.6 r40hda5aaf8_2
        - r-base 4.0.0 h588f722_3
        - r-boot 1.3_25 r40h6115d3f_1
        - r-broom 0.5.6 r40h6115d3f_1
        - r-cli 2.0.2 r40h6115d3f_1
        - r-clipr 0.7.0 r40h6115d3f_1
        - r-crayon 1.3.4 r40h6115d3f_1003
        - r-digest 0.6.25 r40h796a38f_2
        - r-dplyr 0.8.5 r40h796a38f_1
        - r-ellipsis 0.3.0 r40hda5aaf8_1
        - r-fansi 0.4.1 r40hda5aaf8_1
        - r-forcats 0.5.0 r40h6115d3f_1
        - r-generics 0.0.2 r40h6115d3f_1003
        - r-glue 1.4.0 r40hda5aaf8_1
        - r-haven 2.2.0 r40h1d849e9_1
        - r-hms 0.5.3 r40h6115d3f_1
        - r-jomo 2.6_10 r40hda5aaf8_1
        - r-lattice 0.20_41 r40hda5aaf8_2
        - r-lifecycle 0.2.0 r40h6115d3f_1
        - r-lme4 1.1_23 r40h796a38f_1
        - r-magrittr 1.5 r40h6115d3f_1003
        - r-mass 7.3_51.6 r40hda5aaf8_2
        - r-matrix 1.2_18 r40h0adb578_3
        - r-mgcv 1.8_31 r40h0adb578_1
        - r-mice 3.8.0 r40h796a38f_1
        - r-minqa 1.2.4 r40h5b3a9a7_1005
        - r-mitml 0.3_7 r40h6115d3f_2
        - r-nlme 3.1_147 r40h17ddedb_1
        - r-nloptr 1.2.2.1 r40h796a38f_1
        - r-nnet 7.3_14 r40hda5aaf8_1
        - r-numderiv 2016.8_1.1 r40h6115d3f_2
        - r-ordinal 2019.12_10 r40hda5aaf8_1
        - r-pan 1.6 r40h17ddedb_3
        - r-pillar 1.4.4 r40h6115d3f_0
        - r-pkgconfig 2.0.3 r40h6115d3f_1
        - r-plogr 0.2.0 r40h6115d3f_1003
        - r-plyr 1.8.6 r40h796a38f_1
        - r-purrr 0.3.4 r40hda5aaf8_1
        - r-r6 2.4.1 r40h6115d3f_1
        - r-rcpp 1.0.4.6 r40h796a38f_1
        - r-rcppeigen 0.3.3.7.0 r40h796a38f_1
        - r-readr 1.3.1 r40h796a38f_1003
        - r-reshape2 1.4.4 r40h796a38f_1
        - r-rlang 0.4.6 r40hda5aaf8_0
        - r-rpart 4.1_15 r40hda5aaf8_2
        - r-statmod 1.4.34 r40h8933c1f_1
        - r-stringi 1.4.6 r40h796a38f_2
        - r-stringr 1.4.0 r40h6115d3f_2
        - r-survival 3.1_12 r40hda5aaf8_1
        - r-tibble 3.0.1 r40hda5aaf8_1
        - r-tidyr 1.0.2 r40h796a38f_1
        - r-tidyselect 1.0.0 r40h6115d3f_1
        - r-ucminf 1.1_4 r40h17ddedb_1005
        - r-utf8 1.1.4 r40hda5aaf8_1003
        - r-vctrs 0.2.4 r40hda5aaf8_1
        - r-zeallot 0.1.0 r40h6115d3f_1002
        - vc 14.1 h869be7e_1
        - vs2015_runtime 14.16.27012 h30e32a0_2
    run:
        - m2w64-gcc-libs
        - r-base >=4.0,<4.1.0a0
        - r-mgcv
        - r-mice
test:
    commands:
        - '"%R%" -e "library(''logistf'')"'
about:
    home: http://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical-software/fllogistf/
    license: GPL
    license_family: GPL
    license_file: D:\bld\r-logistf_1588774918374\_h_env/lib/R/share/licenses/GPL-3
    summary: Fit a logistic regression model using Firth's bias reduction method,
        equivalent to penalization of the log-likelihood by the Jeffreys  prior. Confidence
        intervals for regression coefficients can be computed by penalized profile
        likelihood. Firth's method was proposed as ideal solution to the problem of
        separation in logistic regression. If needed, the bias reduction can be turned
        off such that ordinary maximum likelihood logistic regression is obtained.
extra:
    copy_test_source_files: true
    final: true
    recipe-maintainers:
        - conda-forge/r
        - slacalle
