{% set version = '6.3.2' %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

package:
  name: r-mixomics
  version: {{ version|replace("-", "_") }}

source:
  fn: mixOmics_{{ version }}.tar.gz
  url:
    - {{ cran_mirror }}/src/contrib/mixOmics_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/mixOmics/mixOmics_{{ version }}.tar.gz
  sha256: 62c9bfe8967997c673e45433a8705dd43f6b5eb3c9fd62529461d3e6f4cb10d3

build:
  merge_build_host: True  # [win]
  number: 2
  # r-rgl is not available on osx b/c libglu dependency not available
  skip: true  # [win32 or osx]
  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - {{posix}}zip               # [win]
  host:
    - r-base
    - r-mass
    - r-rcolorbrewer
    - r-corpcor
    - r-dplyr
    - r-ellipse
    - r-ggplot2
    - r-gridextra
    - r-igraph
    - r-lattice
    - r-matrixstats
    - r-rarpack
    - r-reshape2
    - r-rgl
    - r-tidyr
  run:
    - r-base
    - r-mass
    - r-rcolorbrewer
    - r-corpcor
    - r-dplyr
    - r-ellipse
    - r-ggplot2
    - r-gridextra
    - r-igraph
    - r-lattice
    - r-matrixstats
    - r-rarpack
    - r-reshape2
    - r-rgl
    - r-tidyr

test:
  commands:
    - $R -e "library('mixOmics')"           # [not win]
    - "\"%R%\" -e \"library('mixOmics')\""  # [win]

about:
  home: http://www.mixOmics.org
  license: GPL (>= 2)
  summary: 'Multivariate methods are well suited to large omics data sets where the number of
    variables (e.g. genes, proteins, metabolites) is much larger than the number of
    samples (patients, cells, mice). They have the appealing properties of reducing
    the dimension of the data by using instrumental variables (components), which are
    defined as combinations of all variables. Those components are then used to produce
    useful graphical outputs that enable better understanding of the relationships and
    correlation structures between the different data sets that are integrated. mixOmics
    offers a wide range of multivariate methods for the exploration and integration
    of biological datasets with a particular focus on variable selection. The package
    proposes several sparse multivariate models we have developed to identify the key
    variables that are highly correlated, and/or explain the biological outcome of interest.
    The data that can be analysed with mixOmics may come from high throughput sequencing
    technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics
    etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented
    in mixOmics can also handle missing values without having to delete entire rows
    with missing data. A non exhaustive list of methods include variants of generalised
    Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant
    Analysis. Recently we implemented integrative methods to combine multiple data sets:
    N-integration with variants of Generalised Canonical Correlation Analysis and P-integration
    with variants of multi-group Partial Least Squares.'
  license_family: GPL3
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-3'

extra:
  recipe-maintainers:
    - conda-forge/r
    - MathiasHaudgaard
    - FrodePedersen
    - ArneKr
    - johanneskoester
    - bgruening
    - daler
    - jdblischak
    - cbrueffer
