# This file created by conda-build 3.18.9
# meta.yaml template originally from:
# C:\projects\r-mcmcprecision-feedstock\recipe, last modified Sat Aug 10 08:12:38 2019
# ------------------------------------------------

package:
    name: r-mcmcprecision
    version: 0.3.9
source:
    sha256: 90bb5874c3d39269ffab935517698b420faf73d8bbec81b78f38dccca7f8b4fe
    url:
        - https://cran.r-project.org/src/contrib/Archive/MCMCprecision/MCMCprecision_0.3.9.tar.gz
        - https://cran.r-project.org/src/contrib/MCMCprecision_0.3.9.tar.gz
build:
    merge_build_host: true
    number: '0'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r36h796a38f_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 2019.4 245
        - libblas 3.8.0 11_mkl
        - liblapack 3.8.0 11_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 2019.4 245
        - msys2-conda-epoch 20160418 1
        - r-base 3.6.1 he73377c_3
        - r-combinat 0.0_8 r36h6115d3f_1002
        - r-lattice 0.20_38 r36hda5aaf8_1002
        - r-matrix 1.2_17 r36hda5aaf8_1
        - r-rcpp 1.0.2 r36h796a38f_0
        - r-rcpparmadillo 0.9.500.2.0 r36h796a38f_1
        - r-rcppeigen 0.3.3.5.0 r36h796a38f_1
        - r-rcppprogress 0.4.1 r36h6115d3f_1
    run:
        - m2w64-gcc-libs
        - r-base >=3.6,<3.7.0a0
        - r-combinat
        - r-matrix
        - r-rcpp
        - r-rcpparmadillo
        - r-rcppeigen
        - r-rcppprogress
test:
    commands:
        - '"%R%" -e "library(''MCMCprecision'')"'
about:
    home: https://github.com/danheck/MCMCprecision
    license: GPL-3
    license_family: GPL3
    license_file: C:\bld\r-mcmcprecision_1565424927070\_h_env/lib/R/share/licenses/GPL-3
    summary: Estimates the precision of transdimensional Markov chain Monte Carlo  (MCMC)
        output, which is often used for Bayesian analysis of models with different  dimensionality
        (e.g., model selection). Transdimensional MCMC (e.g., reversible  jump MCMC)
        relies on sampling a discrete model-indicator variable to estimate  the posterior
        model probabilities. If only few switches occur between the models,  precision
        may be low and assessment based on the assumption of independent  samples
        misleading. Based on the observed transition matrix of the indicator  variable,
        the method of Heck, Overstall, Gronau, & Wagenmakers (2018,  Statistics &
        Computing) <doi:10.1007/s11222-018-9828-0> draws posterior samples  of the
        stationary distribution to (a) assess the uncertainty in the estimated  posterior
        model probabilities and (b) estimate the effective sample size of  the MCMC
        output.
extra:
    copy_test_source_files: true
    final: true
    recipe-maintainers:
        - conda-forge/r
