bioconda-recipes/recipes/r-nmf/meta.yaml at 76fdec5fa22c8e7d177a65df4ffee7038dc32882 · bioconda/bioconda-recipes · GitHub

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{% set version = "0.28" %}

package:
  name: r-nmf
  version: '{{ version }}'

source:
  url:
    - {{ cran_mirror }}/src/contrib/NMF_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/NMF/NMF_{{ version }}.tar.gz
  sha256: 77dfe7b323ee5e5f8801851d1d4356932e2ffc810a7ac7faf5542cbfd92eeefb

build:
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/
  run_exports:
    - {{ pin_subpackage('r-nmf', max_pin="x.x") }}

requirements:
  build:
    - cross-r-base {{ r_base }}  # [build_platform != target_platform]
    - {{ compiler('c') }}
    - {{ compiler('cxx') }}
    - {{ stdlib('c') }}
    - make
  host:
    # Depends:
    - r-base >=3.0.0
    - r-registry
    - r-rngtools >=1.2.3
    - r-cluster
    # Imports:
    - r-stringr >=1.0.0
    - r-digest
    - r-gridbase
    - r-colorspace
    - r-rcolorbrewer
    - r-foreach
    - r-doparallel
    - r-ggplot2
    - r-reshape2
    - bioconductor-biobase
    - r-biocmanager
  run:
    # Depends:
    - r-base >=3.0.0
    - r-registry
    - r-rngtools >=1.2.3
    - r-cluster
    # Imports:
    - r-stringr >=1.0.0
    - r-digest
    - r-gridbase
    - r-colorspace
    - r-rcolorbrewer
    - r-foreach
    - r-doparallel
    - r-ggplot2
    - r-reshape2
    - bioconductor-biobase
    - r-biocmanager

test:
  commands:
    - $R -e "library('NMF')"

about:
  home: https://renozao.github.io/NMF/
  dev_url: https://github.com/renozao/NMF
  doc_url: https://cran.r-project.org/package=NMF
  license: GPL-2.0-or-later
  license_family: GPL3
  license_file:
    - '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2'
    - '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-3'
  summary: Framework to perform Non-negative Matrix Factorization (NMF).
  description: |
    Provides a framework to perform Non-negative Matrix Factorization (NMF). The package
    implements a set of already published algorithms and seeding methods, and provides a
    framework to test, develop and plug new/custom algorithms. Most of the built-in algorithms
    have been optimized in C++, and the main interface function provides an easy way of
    performing parallel computations on multicore machines.

extra:
  skip-lints:
    - in_other_channels
  recipe-maintainers:
    - tfenne
  additional-platforms:
    - linux-aarch64
    - osx-arm64