# This file created by conda-build 3.21.8
# meta.yaml template originally from:
# /opt/recipe, last modified Thu Nov  3 12:09:57 2022
# ------------------------------------------------

package:
  name: bioconductor-depecher
  version: 1.14.0
source:
  md5: 6e69a75fc559cdaf7d8e7bd2216edb92
  url:
    - https://bioarchive.galaxyproject.org/DepecheR_1.14.0.tar.gz
    - https://bioconductor.org/packages/3.16/bioc/src/contrib/DepecheR_1.14.0.tar.gz
    - https://depot.galaxyproject.org/software/bioconductor-depecher/bioconductor-depecher_1.14.0_src_all.tar.gz
build:
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r42hc247a5b_0
requirements:
  build:
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    - make 4.3 hd18ef5c_1
    - mesa-dri-drivers-cos6-x86_64 11.0.7 h9b0a68f_1105
    - mesa-dri1-drivers-cos6-x86_64 7.11 h9b0a68f_1105
    - mesa-libgl-cos6-x86_64 11.0.7 h9b0a68f_1105
    - mesa-libgl-devel-cos6-x86_64 11.0.7 h9b0a68f_1105
    - pthread-stubs 0.4 h36c2ea0_1001
    - sysroot_linux-64 2.12 he073ed8_15
    - xorg-fixesproto 5.0 h7f98852_1002
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  host:
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  run:
    - bioconductor-mixomics >=6.22.0,<6.23.0
    - libblas >=3.9.0,<4.0a0
    - libgcc-ng >=12
    - liblapack >=3.9.0,<4.0a0
    - libstdcxx-ng >=12
    - r-base >=4.2,<4.3.0a0
    - r-beanplot >=1.2
    - r-dosnow >=1.0.16
    - r-dplyr >=0.7.8
    - r-fnn >=1.1.3
    - r-foreach >=1.4.4
    - r-ggplot2 >=3.1.0
    - r-gmodels >=2.18.1
    - r-gplots >=3.0.1
    - r-mass >=7.3.51
    - r-matrixstats >=0.54.0
    - r-moments >=0.14
    - r-rcpp >=1.0.0
    - r-rcppeigen
    - r-reshape2 >=1.4.3
    - r-robustbase >=0.93.5
    - r-viridis >=0.5.1
test:
  commands:
    - LD_LIBRARY_PATH="${BUILD_PREFIX}/x86_64-conda-linux-gnu/sysroot/usr/lib64:${BUILD_PREFIX}/lib"
      $R -e "library('DepecheR')"
about:
  description: The purpose of this package is to identify traits in a dataset that
    can separate groups. This is done on two levels. First, clustering is performed,
    using an implementation of sparse K-means. Secondly, the generated clusters are
    used to predict outcomes of groups of individuals based on their distribution
    of observations in the different clusters. As certain clusters with separating
    information will be identified, and these clusters are defined by a sparse number
    of variables, this method can reduce the complexity of data, to only emphasize
    the data that actually matters.
  home: https://bioconductor.org/packages/3.16/bioc/html/DepecheR.html
  license: MIT + file LICENSE
  license_file: LICENSE
  summary: Determination of essential phenotypic elements of clusters in high-dimensional
    entities
extra:
  container:
    extended-base: true
  copy_test_source_files: true
  final: true
