# This file created by conda-build 3.18.8
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Fri Jul 19 12:48:50 2019
# ------------------------------------------------

package:
    name: r-msm
    version: 1.6.7
source:
    sha256: 7503c0f61916033ed0efad54727368bce629ff2d370f302b71bc1cb924d2e23a
    url:
        - https://cran.r-project.org/src/contrib/Archive/msm/msm_1.6.7.tar.gz
        - https://cran.r-project.org/src/contrib/msm_1.6.7.tar.gz
build:
    merge_build_host: true
    number: '1'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r36hda5aaf8_1
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 8_mkl
        - liblapack 3.8.0 8_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 h91c7823_2
        - r-expm 0.999_4 r36h17ddedb_1
        - r-lattice 0.20_38 r36hda5aaf8_1001
        - r-matrix 1.2_17 r36hda5aaf8_1
        - r-mvtnorm 1.0_11 r36h17ddedb_1
        - r-survival 2.44_1.1 r36hda5aaf8_1
    run:
        - m2w64-gcc-libs
        - r-base >=3.6,<3.7.0a0
        - r-expm
        - r-mvtnorm
        - r-survival
test:
    commands:
        - '"%R%" -e "library(''msm'')"'
about:
    home: https://github.com/chjackson/msm
    license: GPL (>= 2)
    license_family: GPL3
    license_file: D:\bld\r-msm_1563540705813\_h_env/lib/R/share/licenses/GPL-2
    summary: Functions for fitting continuous-time Markov and hidden Markov multi-state
        models to longitudinal data.  Designed for processes observed at arbitrary
        times in continuous time (panel data) but some other observation schemes are
        supported. Both Markov transition rates and the hidden Markov output process
        can be modelled in terms of covariates, which may be constant or piecewise-constant
        in time.
extra:
    copy_test_source_files: true
    final: true
    recipe-maintainers:
        - conda-forge/r
        - npavlovikj
