# This file created by conda-build 3.19.1
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Thu Mar 19 15:47:17 2020
# ------------------------------------------------

package:
    name: r-brglm2
    version: 0.6.2
source:
    sha256: 3f69bb1502e37f333024c08ef6dd78066a77f68393108df3b25483736e68578d
    url:
        - https://cran.r-project.org/src/contrib/Archive/brglm2/brglm2_0.6.2.tar.gz
        - https://cran.r-project.org/src/contrib/brglm2_0.6.2.tar.gz
build:
    merge_build_host: true
    number: '0'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r36h6115d3f_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 2020.0 166
        - libblas 3.8.0 15_mkl
        - 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-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-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 2020.0 166
        - msys2-conda-epoch 20160418 1
        - r-base 3.6.2 hf0ea6f9_1
        - r-enrichwith 0.3.1 r36h6115d3f_0
        - r-lattice 0.20_40 r36hda5aaf8_0
        - r-lpsolveapi 5.5.2.0_17.6 r36hda5aaf8_0
        - r-mass 7.3_51.5 r36hda5aaf8_0
        - r-matrix 1.2_18 r36h0adb578_2
        - r-nnet 7.3_13 r36hda5aaf8_0
        - r-numderiv 2016.8_1.1 r36h6115d3f_1
    run:
        - r-base >=3.6,<3.7.0a0
        - 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
    license_family: GPL3
    license_file:
        - D:\bld\r-brglm2_1584632991640\_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
