# This file created by conda-build 3.19.2
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Thu May 14 18:54:19 2020
# ------------------------------------------------

package:
    name: r-mcmcprecision
    version: 0.4.0
source:
    sha256: 4e419c325a9e66cab67ea2c141062791bd52a6e6308395731b5307b323641864
    url:
        - https://cran.r-project.org/src/contrib/Archive/MCMCprecision/MCMCprecision_0.4.0.tar.gz
        - https://cran.r-project.org/src/contrib/MCMCprecision_0.4.0.tar.gz
build:
    merge_build_host: true
    number: '2'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r36hb8e368e_2
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
        - libblas 3.8.0 8_h8933c1f_netlib
        - liblapack 3.8.0 8_h8933c1f_netlib
        - 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
        - msys2-conda-epoch 20160418 1
        - r-base 3.6.3 hf0ea6f9_2
        - r-combinat 0.0_8 r36h6115d3f_1003
        - r-lattice 0.20_41 r36hda5aaf8_2
        - r-matrix 1.2_18 r36h0adb578_3
        - r-rcpp 1.0.4.6 r36h796a38f_1
        - r-rcpparmadillo 0.9.870.2.0 r36hb8e368e_1
        - r-rcppeigen 0.3.3.7.0 r36hb8e368e_2
        - r-rcppprogress 0.4.2 r36h6115d3f_1
    run:
        - libblas >=3.8.0,<4.0a0
        - liblapack >=3.8.0,<3.9.0a0
        - 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: D:\bld\r-mcmcprecision_1589482611914\_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
