# This file created by conda-build 3.21.6
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Sun Nov 21 20:36:53 2021
# ------------------------------------------------

package:
  name: r-brglm2
  version: 0.8.2
source:
  sha256: 4eae58304ce081feb8c0bea9a6021cec5ccb2ecd455068e88d30aba7566db418
  url:
    - https://cran.r-project.org/src/contrib/Archive/brglm2/brglm2_0.8.2.tar.gz
    - https://cran.r-project.org/src/contrib/brglm2_0.8.2.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r40h6d2157b_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 2021.4.0 h57928b3_3556
    - libblas 3.9.0 12_win64_mkl
    - liblapack 3.9.0 12_win64_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 2021.4.0 h0e2418a_729
    - msys2-conda-epoch 20160418 1
    - r-base 4.0.5 hddad469_2
    - r-enrichwith 0.3.1 r40hc72bb7e_1
    - r-lattice 0.20_45 r40h6d2157b_0
    - r-lpsolveapi 5.5.2.0_17.7 r40h7af7814_1
    - r-mass 7.3_54 r40h6d2157b_0
    - r-matrix 1.3_4 r40hd2e18ed_0
    - r-nnet 7.3_16 r40h6d2157b_0
    - r-numderiv 2016.8_1.1 r40hc72bb7e_3
    - tbb 2021.4.0 h2d74725_1
    - ucrt 10.0.20348.0 h57928b3_0
    - vc 14.2 hb210afc_5
    - vs2015_runtime 14.29.30037 h902a5da_5
  run:
    - m2w64-gcc-libs
    - r-base >=4.0,<4.1.0a0
    - 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_1637527442875\_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
