# This file created by conda-build 3.21.4
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Mon Sep 27 17:50:43 2021
# ------------------------------------------------

package:
  name: r-brglm2
  version: 0.8.1
source:
  sha256: fc5532320bbf00c7487cc8a182362f6db161d7314e0b7d184d74389e8edb0bff
  url:
    - https://cran.r-project.org/src/contrib/Archive/brglm2/brglm2_0.8.1.tar.gz
    - https://cran.r-project.org/src/contrib/brglm2_0.8.1.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r41h6d2157b_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 2021.3.0 h57928b3_3372
    - 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-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 2020.4 hb70f87d_311
    - msys2-conda-epoch 20160418 1
    - r-base 4.1.1 hddad469_1
    - r-enrichwith 0.3.1 r41hc72bb7e_1
    - r-lattice 0.20_45 r41h6d2157b_0
    - r-lpsolveapi 5.5.2.0_17.7 r41h6d2157b_0
    - r-mass 7.3_54 r41h6d2157b_0
    - r-matrix 1.3_4 r41hd2e18ed_0
    - r-nnet 7.3_16 r41h6d2157b_0
    - r-numderiv 2016.8_1.1 r41hc72bb7e_3
  run:
    - m2w64-gcc-libs
    - r-base >=4.1,<4.2.0a0
    - r-enrichwith
    - r-lpsolveapi
    - r-mass
    - r-matrix
    - r-nnet
    - r-numderiv
test:
  commands:
    - '"%R%" -e "library(''brglm2'')"'
about:
  home: https://github.com/ikosmidis/brglm2
  license: GPL-3.0-only
  license_family: GPL3
  license_file:
    - D:\bld\r-brglm2_1632765254424\_h_env/lib/R/share/licenses/GPL-3
  summary: Estimation and inference from generalized linear models based on various
    methods for bias reduction and maximum penalized likelihood with powers of the
    Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction
    of estimation bias by solving either the mean bias-reducing adjusted score equations
    in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>,
    or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>,
    or through the direct subtraction of an estimate of the bias of the maximum likelihood
    estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991)
    <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6>
    for more details. Estimation in all cases takes place via a quasi Fisher scoring
    algorithm, and S3 methods for the construction of of confidence intervals for
    the reduced-bias estimates are provided. In the special case of generalized linear
    models for binomial and multinomial responses (both ordinal and nominal), the
    adjusted score approaches to mean and media bias reduction have been found to
    return estimates with improved frequentist properties, that are also always finite,
    even in cases where the maximum likelihood estimates are infinite (e.g. complete
    and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>,
    for a proof for mean bias reduction in logistic regression).
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - conda-forge/r
    - taylorreiter
