# This file created by conda-build 2.1.18
# meta.yaml template originally from:
# C:\projects\r-ebayesthresh-feedstock\recipe, last modified Tue Dec 19 01:49:54 2017
# ------------------------------------------------

package:
    name: r-ebayesthresh
    version: '1.4_12'
source:
    fn: EbayesThresh_1.4-12.tar.gz
    sha256: 28850348aa0752a75e0c8f1ae20ef7e352a6493abe8b6a6c40ac09f5476b421d
    url:
        - https://cran.r-project.org/src/contrib/EbayesThresh_1.4-12.tar.gz
        - https://cran.r-project.org/src/contrib/Archive/EbayesThresh/EbayesThresh_1.4-12.tar.gz
build:
    noarch: ''
    noarch_python: false
    number: '0'
    rpaths:
        - lib/R/lib/
        - lib/
    string: r3.3.2_0
requirements:
    build:
        - m2-msys2-runtime 2.5.0.17080.65c939c 3
        - m2-libcrypt 1.3 2
        - m2-libexpat 2.1.1 2
        - m2-gcc-libs 5.3.0 4
        - m2-heimdal-libs 1.5.3 10
        - m2-libidn 1.32 2
        - m2-info 6.0 2
        - m2w64-make 4.1.2351.a80a8b8 2
        - m2w64-libxml2 2.9.3 4
        - m2-bash 4.3.042 5
        - r-mass 7.3_45 r3.3.2_0
        - m2-ncurses 6.0.20160220 2
        - m2-libintl 0.19.7 4
        - m2w64-speexdsp 1.2rc3 3
        - m2-icu 56.1 2
        - m2w64-tktable 2.10 5
        - m2w64-headers-git 5.0.0.4636.c0ad18a 2
        - m2-gmp 6.1.0 3
        - m2w64-fftw 3.3.4 6
        - m2w64-gettext 0.19.7 2
        - m2w64-gcc-objc 5.3.0 6
        - m2w64-mpfr 3.1.4 4
        - r-wavethresh 4.6.8 r3.3.2_0
        - m2-gzip 1.7 2
        - m2-findutils 4.6.0 2
        - m2-libdb 5.3.28 3
        - m2w64-gcc-ada 5.3.0 6
        - m2-libgdbm 1.11 4
        - m2-texinfo 6.0 2
        - m2-curl 7.47.1 3
        - m2w64-binutils 2.25.1 5
        - r-base 3.3.2 5
        - m2-libpcre 8.38 2
        - m2-libtasn1 4.7 2
        - m2-libp11-kit 0.23.2 2
        - m2-p11-kit 0.23.2 2
        - m2w64-toolchain 5.3.0 7
        - m2w64-gcc-libgfortran 5.3.0 6
        - m2-db 5.3.28 3
        - m2-coreutils 8.25 102
        - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
        - m2w64-wineditline 2.101 5
        - m2w64-libogg 1.3.2 3
        - m2w64-mpc 1.0.3 3
        - m2-libiconv 1.14 3
        - m2-openssl 1.0.2.g 2
        - m2w64-libjpeg-turbo 1.4.2 3
        - m2w64-pcre 8.38 2
        - m2w64-libpng 1.6.21 2
        - m2w64-isl 0.16.1 2
        - m2w64-crt-git 5.0.0.4636.2595836 2
        - m2-texinfo-tex 6.0 2
        - m2-perl 5.22.1 2
        - m2-libmetalink 0.1.2 3
        - m2w64-libtiff 4.0.6 2
        - m2w64-windows-default-manifest 6.4 3
        - m2w64-bzip2 1.0.6 6
        - m2w64-tcl 8.6.5 3
        - m2w64-libmangle-git 5.0.0.4509.2e5a9a2 2
        - m2w64-tk 8.6.5 3
        - m2-libffi 3.2.1 2
        - m2-less 481 2
        - m2w64-pkg-config 0.29.1 2
        - m2w64-bwidget 1.9.10 2
        - m2w64-zlib 1.2.8 10
        - m2w64-gcc-libs 5.3.0 7
        - m2-p7zip 9.38.1 2
        - m2w64-gsl 2.1 2
        - m2w64-winpthreads-git 5.0.0.4634.697f757 2
        - m2-libcurl 7.47.1 3
        - m2-libopenssl 1.0.2.g 2
        - m2-ca-certificates 20150426 103
        - m2-zlib 1.2.8 4
        - m2-sed 4.2.2 3
        - m2w64-tools-git 5.0.0.4592.90b8472 2
        - m2w64-flac 1.3.1 3
        - m2w64-libvorbis 1.3.5 2
        - m2-libreadline 6.3.008 8
        - msys2-conda-epoch 20160418 1
        - m2w64-gcc-fortran 5.3.0 6
        - m2w64-xz 5.2.2 2
        - m2-gdbm 1.11 4
        - m2w64-libiconv 1.14 6
        - m2w64-gcc-libs-core 5.3.0 7
        - m2-libsqlite 3.10.0.0 2
        - m2-libssh2 1.6.0 2
        - m2w64-gcc 5.3.0 6
        - m2w64-expat 2.1.1 2
        - m2-libedit 3.1 20150326
        - m2w64-speex 1.2rc2 3
        - m2w64-gmp 6.1.0 2
        - m2w64-libsndfile 1.0.26 2
    run:
        - r-base
        - r-wavethresh
test:
    commands:
        - '"%R%" -e "library(''EbayesThresh'')"'
about:
    home: https://github.com/stephenslab/EbayesThresh
    license: GPL (>= 2)
    license_family: GPL3
    license_file: C:\bld\r-ebayesthresh_1513648314226\_b_env\R\share\licenses\GPL-3
    summary: Empirical Bayes thresholding using the methods developed by I. M. Johnstone
        and B. W. Silverman. The basic problem is to estimate a mean vector given
        a vector of observations of the mean vector plus white noise, taking advantage
        of possible sparsity in the mean vector. Within a Bayesian formulation, the
        elements of the mean vector are modelled as having, independently, a distribution
        that is a mixture of an atom of probability at zero and a suitable heavy-tailed
        distribution. The mixing parameter can be estimated by a marginal maximum
        likelihood approach. This leads to an adaptive thresholding approach on the
        original data. Extensions of the basic method, in particular to wavelet thresholding,
        are also implemented within the package.
extra:
    final: true
    recipe-maintainers:
        - johanneskoester
        - bgruening
        - daler
        - jdblischak
