# This file created by conda-build 3.17.8
# meta.yaml template originally from:
# /opt/recipe, last modified Sun May 12 04:32:30 2019
# ------------------------------------------------

package:
    name: bioconductor-ledpred
    version: 1.18.0
source:
    md5: a8c110446d86b622593f1326c4757d3c
    url:
        - https://bioarchive.galaxyproject.org/LedPred_1.18.0.tar.gz
        - https://bioconductor.org/packages/3.9/bioc/src/contrib/LedPred_1.18.0.tar.gz
        - https://depot.galaxyproject.org/software/bioconductor-ledpred/bioconductor-ledpred_1.18.0_src_all.tar.gz
build:
    noarch: generic
    number: '0'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r351_0
requirements:
    host:
        - _r-mutex 1.0.0 anacondar_1
        - binutils_impl_linux-64 2.31.1 h6176602_1
        - binutils_linux-64 2.31.1 h6176602_7
        - bwidget 1.9.11 1
        - bzip2 1.0.6 h14c3975_1002
        - ca-certificates 2019.3.9 hecc5488_0
        - cairo 1.16.0 ha4e643d_1000
        - curl 7.64.1 hf8cf82a_0
        - fontconfig 2.13.1 he4413a7_1000
        - freetype 2.10.0 he983fc9_0
        - gcc_impl_linux-64 7.3.0 habb00fd_1
        - gcc_linux-64 7.3.0 h553295d_7
        - gettext 0.19.8.1 hc5be6a0_1002
        - gfortran_impl_linux-64 7.3.0 hdf63c60_1
        - gfortran_linux-64 7.3.0 h553295d_7
        - glib 2.58.3 hf63aee3_1001
        - graphite2 1.3.13 hf484d3e_1000
        - gsl 2.4 h294904e_1006
        - gxx_impl_linux-64 7.3.0 hdf63c60_1
        - gxx_linux-64 7.3.0 h553295d_7
        - harfbuzz 2.4.0 h37c48d4_0
        - icu 58.2 hf484d3e_1000
        - jpeg 9c h14c3975_1001
        - krb5 1.16.3 h05b26f9_1001
        - libblas 3.8.0 8_openblas
        - libcblas 3.8.0 8_openblas
        - libcurl 7.64.1 hda55be3_0
        - libedit 3.1.20170329 hf8c457e_1001
        - libffi 3.2.1 he1b5a44_1006
        - libgcc-ng 8.2.0 hdf63c60_1
        - libgfortran-ng 7.3.0 hdf63c60_0
        - libiconv 1.15 h516909a_1005
        - libpng 1.6.37 hed695b0_0
        - libssh2 1.8.2 h22169c7_2
        - libstdcxx-ng 8.2.0 hdf63c60_1
        - libtiff 4.0.10 h648cc4a_1001
        - libuuid 2.32.1 h14c3975_1000
        - libxcb 1.13 h14c3975_1002
        - libxml2 2.9.9 h13577e0_0
        - make 4.2.1 h14c3975_2004
        - ncurses 6.1 hf484d3e_1002
        - openblas 0.3.6 h6e990d7_2
        - openssl 1.1.1b h14c3975_1
        - pango 1.40.14 h4ea9474_1004
        - pcre 8.41 hf484d3e_1003
        - pixman 0.34.0 h14c3975_1003
        - pthread-stubs 0.4 h14c3975_1001
        - r-akima 0.6_2 r351h9ac9557_1001
        - r-assertthat 0.2.1 r351h6115d3f_0
        - r-base 3.5.1 h271c98b_1006
        - r-bitops 1.0_6 r351h96ca727_1002
        - r-catools 1.17.1.2 r351h29659fb_0
        - r-class 7.3_15 r351h96ca727_1000
        - r-cli 1.1.0 r351h6115d3f_0
        - r-colorspace 1.4_1 r351hcdcec82_0
        - r-crayon 1.3.4 r351h6115d3f_1001
        - r-digest 0.6.18 r351h96ca727_1000
        - r-e1071 1.7_0 r351h29659fb_1000
        - r-fansi 0.4.0 r351h96ca727_1000
        - r-gdata 2.18.0 r351h6115d3f_1001
        - r-ggplot2 3.1.1 r351h6115d3f_0
        - r-glue 1.3.1 r351hcdcec82_0
        - r-gplots 3.0.1.1 r351h6115d3f_0
        - r-gtable 0.3.0 r351h6115d3f_0
        - r-gtools 3.8.1 r351h96ca727_1002
        - r-irr 0.84.1 r351h6115d3f_0
        - r-jsonlite 1.6 r351h96ca727_1000
        - r-kernsmooth 2.23_15 r351ha65eedd_1002
        - r-labeling 0.3 r351h6115d3f_1001
        - r-lattice 0.20_38 r351h96ca727_1000
        - r-lazyeval 0.2.2 r351hcdcec82_0
        - r-lpsolve 5.6.13 r351h96ca727_1002
        - r-magrittr 1.5 r351h6115d3f_1001
        - r-mass 7.3_51.4 r351hcdcec82_0
        - r-matrix 1.2_17 r351hcdcec82_0
        - r-mgcv 1.8_28 r351hcdcec82_0
        - r-misc3d 0.8_4 r351_2001
        - r-munsell 0.5.0 r351h6115d3f_1001
        - r-nlme 3.1_139 r351h9bbef5b_0
        - r-pillar 1.3.1 r351h6115d3f_1000
        - r-pkgconfig 2.0.2 r351h6115d3f_1001
        - r-plot3d 1.1.1 r351_1000
        - r-plyr 1.8.4 r351h29659fb_1002
        - r-praise 1.0.0 r351h6115d3f_1001
        - r-r6 2.4.0 r351h6115d3f_0
        - r-rcolorbrewer 1.1_2 r351h6115d3f_1001
        - r-rcpp 1.0.1 r351h0357c0b_0
        - r-rcurl 1.95_4.12 r351hcdcec82_0
        - r-reshape2 1.4.3 r351h29659fb_1003
        - r-rlang 0.3.4 r351hcdcec82_0
        - r-rocr 1.0_7 r351h6115d3f_1001
        - r-scales 1.0.0 r351h29659fb_1001
        - r-sp 1.3_1 r351h96ca727_1000
        - r-stringi 1.4.3 r351h0357c0b_0
        - r-stringr 1.4.0 r351h6115d3f_0
        - r-testthat 2.1.1 r351h0357c0b_0
        - r-tibble 2.1.1 r351hcdcec82_0
        - r-utf8 1.1.4 r351h96ca727_1000
        - r-viridislite 0.3.0 r351h6115d3f_1001
        - r-withr 2.1.2 r351h6115d3f_1000
        - readline 7.0 hf8c457e_1001
        - tk 8.6.9 h84994c4_1001
        - tktable 2.10 h14c3975_0
        - xorg-kbproto 1.0.7 h14c3975_1002
        - xorg-libice 1.0.9 h516909a_1004
        - xorg-libsm 1.2.3 h84519dc_1000
        - xorg-libx11 1.6.7 h14c3975_1000
        - xorg-libxau 1.0.9 h14c3975_0
        - xorg-libxdmcp 1.1.3 h516909a_0
        - xorg-libxext 1.3.4 h516909a_0
        - xorg-libxrender 0.9.10 h516909a_1002
        - xorg-renderproto 0.11.1 h14c3975_1002
        - xorg-xextproto 7.3.0 h14c3975_1002
        - xorg-xproto 7.0.31 h14c3975_1007
        - xz 5.2.4 h14c3975_1001
        - zlib 1.2.11 h14c3975_1004
    run:
        - r-akima
        - r-base >=3.5.1,<3.5.2.0a0
        - r-e1071 >=1.6
        - r-ggplot2
        - r-irr
        - r-jsonlite
        - r-plot3d
        - r-plyr
        - r-rcurl
        - r-rocr
        - r-testthat
test:
    commands:
        - $R -e "library('LedPred')"
about:
    home: https://bioconductor.org/packages/3.9/bioc/html/LedPred.html
    license: MIT | file LICENSE
    summary: This package aims at creating a predictive model of regulatory sequences
        used to score unknown sequences based on the content of DNA motifs, next-generation
        sequencing (NGS) peaks and signals and other numerical scores of the sequences
        using supervised classification. The package contains a workflow based on
        the support vector machine (SVM) algorithm that maps features to sequences,
        optimize SVM parameters and feature number and creates a model that can be
        stored and used to score the regulatory potential of unknown sequences.
extra:
    copy_test_source_files: true
    final: true
