# This file created by conda-build 3.20.4
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Tue Oct 20 08:32:37 2020
# ------------------------------------------------

package:
    name: r-brglm2
    version: 0.7.0
source:
    sha256: dc331bd8ad1223f5a9125b2e82d2862fc8e8dfc91397f79e9fb689b33d28b601
    url:
        - https://cran.r-project.org/src/contrib/Archive/brglm2/brglm2_0.7.0.tar.gz
        - https://cran.r-project.org/src/contrib/brglm2_0.7.0.tar.gz
build:
    merge_build_host: true
    number: '0'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r40h852d06f_0
requirements:
    build:
        - m2-gcc-libs 5.3.0 4
        - m2-libbz2 1.0.6 3
        - m2-msys2-runtime 2.5.0.17080.65c939c 3
        - m2-zip 3.0 2
        - msys2-conda-epoch 20160418 1
    host:
        - _r-mutex 1.0.1 anacondar_1
        - intel-openmp 2020.1 216
        - libblas 3.8.0 16_mkl
        - liblapack 3.8.0 16_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 4
        - 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 2020.1 216
        - msys2-conda-epoch 20160418 1
        - r-base 4.0.3 hc562dc2_2
        - r-enrichwith 0.3.1 r40h6115d3f_1
        - r-lattice 0.20_41 r40hda5aaf8_2
        - r-lpsolveapi 5.5.2.0_17.7 r40hda5aaf8_0
        - r-mass 7.3_53 r40hda5aaf8_0
        - r-matrix 1.2_18 r40h0adb578_3
        - r-nnet 7.3_14 r40hda5aaf8_1
        - r-numderiv 2016.8_1.1 r40h6115d3f_3
    run:
        - 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_1603183111118\_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. (2016) <arXiv:1604.04768>, 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)
        <http://www.jstor.org/stable/2345592>. See Kosmidis et al (2019) <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 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). 'brglm2' also
        provides pre-fit and post-fit methods for detecting separation and infinite
        maximum likelihood estimates in binomial response generalized linear models.
extra:
    copy_test_source_files: true
    final: true
    recipe-maintainers:
        - conda-forge/r
        - taylorreiter
