{% set version = "1.10.0" %}
{% set name = "biosigner" %}
{% set bioc = "3.8" %}

package:
  name: 'bioconductor-{{ name|lower }}'
  version: '{{ version }}'
source:
  url:
    - 'https://bioconductor.org/packages/{{ bioc }}/bioc/src/contrib/{{ name }}_{{ version }}.tar.gz'
    - 'https://bioarchive.galaxyproject.org/{{ name }}_{{ version }}.tar.gz'
    - 'https://depot.galaxyproject.org/software/bioconductor-{{ name }}/bioconductor-{{ name }}_{{ version }}_src_all.tar.gz'
  md5: 5a7f57cd3552ed1c2cf21cf18f33da77
build:
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/
  noarch: generic
# Suggests: BioMark, RUnit, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, rmarkdown
requirements:
  host:
    - 'bioconductor-biobase >=2.42.0,<2.43.0'
    - 'bioconductor-ropls >=1.14.0,<1.15.0'
    - r-base
    - r-e1071
    - r-randomforest
  run:
    - 'bioconductor-biobase >=2.42.0,<2.43.0'
    - 'bioconductor-ropls >=1.14.0,<1.15.0'
    - r-base
    - r-e1071
    - r-randomforest
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: CeCILL
  summary: 'Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding ''restricted'' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics.'
extra:
  identifiers:
    - biotools:biosigner
    - doi:10.3389/fmolb.2016.00026
  parent_recipe:
    name: bioconductor-biosigner
    path: recipes/bioconductor-biosigner
    version: 1.8.0

