# This file created by conda-build 2.1.18
# meta.yaml template originally from:
# C:\projects\r-kpeaks-feedstock\recipe, last modified Sat Feb 17 09:20:13 2018
# ------------------------------------------------

package:
    name: r-kpeaks
    version: 0.1.0
source:
    fn: kpeaks_0.1.0.tar.gz
    sha256: 68e6e1a1d2aaac2cd409fb6829995b4ad29a8c32fe8214022c6343ed39b3b7e3
    url:
        - https://cran.r-project.org/src/contrib/kpeaks_0.1.0.tar.gz
        - https://cran.r-project.org/src/contrib/Archive/kpeaks/kpeaks_0.1.0.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
        - 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
        - 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
test:
    commands:
        - '"%R%" -e "library(''kpeaks'')"'
about:
    home: https://CRAN.R-project.org/package=kpeaks
    license: GPL (>= 2)
    license_family: GPL3
    license_file: C:\bld\r-kpeaks_1518859354040\_b_env\R\share\licenses\GPL-3
    summary: The input argument k which is the number of clusters is needed to start
        all of the partitioning clustering algorithms. In unsupervised learning applications,
        an optimal value of this argument is widely determined by using the internal
        validity indexes. Since these indexes suggest a k value which is computed
        on the clustering results after several runs of a clustering algorithm they
        are computationally expensive. On the contrary, 'kpeaks' enables to estimate
        k before running any clustering algorithm. It is based on a simple novel technique
        using the descriptive statistics of peak counts of the features in a data
        set.
extra:
    final: true
    recipe-maintainers:
        - cbrueffer
        - johanneskoester
        - bgruening
        - daler
        - jdblischak
