{% set version = "2.40.0" %}
{% set name = "fabia" %}
{% set bioc = "3.14" %}

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: 4401fcebd478d021d6a5311a7ef28661
build:
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/
requirements:
  host:
    - 'bioconductor-biobase >=2.54.0,<2.55.0'
    - r-base
    - libblas
    - liblapack
  run:
    - 'bioconductor-biobase >=2.54.0,<2.55.0'
    - r-base
  build:
    - {{ compiler('c') }}
    - make
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: 'LGPL (>= 2.1)'
  summary: 'FABIA: Factor Analysis for Bicluster Acquisition'
  description: 'Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C.'
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/LGPL-3'
extra:
  identifiers:
    - biotools:fabia
  parent_recipe:
    name: bioconductor-fabia
    path: recipes/bioconductor-fabia
    version: 2.26.0

