{% set version = "1.3.2" %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/huge_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/huge/huge_{{ version }}.tar.gz
  sha256: 0d4b683c50fb99ee0ef7c3551ac7cca4e828eacd10372853a3037b19e27e23c9

build:
  merge_build_host: True  # [win]
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - {{ compiler('c') }}        # [not win]
    - {{ compiler('cxx') }}      # [not win]
    - {{native}}toolchain        # [win]
    - {{posix}}filesystem        # [win]
    - {{posix}}sed               # [win]
    - {{posix}}grep              # [win]
    - {{posix}}autoconf
    - {{posix}}automake          # [not win]
    - {{posix}}automake-wrapper  # [win]
    - {{posix}}pkg-config
    - {{posix}}make
    - {{posix}}coreutils         # [win]
    - {{posix}}zip               # [win]
  host:
    - r-base
    - r-mass
    - r-matrix
    - r-rcpp
    - r-rcppeigen
    - r-igraph
  run:
    - r-base
    - {{native}}gcc-libs         # [win]
    - r-mass
    - r-matrix
    - r-rcpp
    - r-rcppeigen
    - r-igraph

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

about:
  home: https://CRAN.R-project.org/package=huge
  license: GPL-2
  summary: 'Provides a general framework for high-dimensional undirected graph estimation. It
    integrates data preprocessing, neighborhood screening, graph estimation, and model
    selection techniques into a pipeline. In preprocessing stage, the nonparanormal(npn)
    transformation is applied to help relax the normality assumption. In the graph estimation
    stage, the graph structure is estimated by Meinshausen-Buhlmann graph estimation
    or the graphical lasso, and both methods can be further accelerated by the lossy
    screening rule preselecting the neighborhood of each variable by correlation thresholding.
    We target on high-dimensional data analysis usually d >> n, and the computation
    is memory-optimized using the sparse matrix output. We also provide a computationally
    efficient approach, correlation thresholding graph estimation. Three regularization/thresholding
    parameter selection methods are included in this package: (1)stability approach
    for regularization selection (2) rotation information criterion (3) extended Bayesian
    information criterion which is only available for the graphical lasso.'
  license_family: GPL2
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2'

extra:
  recipe-maintainers:
    - nick-youngblut
    - conda-forge/r

# Package: huge
# Type: Package
# Title: High-Dimensional Undirected Graph Estimation
# Version: 1.3.0
# Author: Haoming Jiang, Xinyu Fei, Yukun Ma, Xingguo Li, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman, Tuo Zhao
# Maintainer: Haoming Jiang <jianghm.ustc@gmail.com>
# Depends: R (>= 3.0.0)
# Imports: Matrix, igraph, MASS, grDevices, graphics, methods, stats, utils, Rcpp
# LinkingTo: Rcpp, RcppEigen
# Description: Provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline. In preprocessing stage, the nonparanormal(npn) transformation is applied to help relax the normality assumption. In the graph estimation stage, the graph structure is estimated by Meinshausen-Buhlmann graph estimation or the graphical lasso, and both methods can be further accelerated by the lossy screening rule preselecting the neighborhood of each variable by correlation thresholding. We target on high-dimensional data analysis usually d >> n, and the computation is memory-optimized using the sparse matrix output. We also provide a computationally efficient approach, correlation thresholding graph estimation. Three regularization/thresholding parameter selection methods are included in this package: (1)stability approach for regularization selection (2) rotation information criterion (3) extended Bayesian information criterion which is only available for the graphical lasso.
# License: GPL-2
# Repository: CRAN
# NeedsCompilation: yes
# RoxygenNote: 6.1.0
# Encoding: UTF-8
# Packaged: 2019-02-20 07:45:15 UTC; Haoming
# Date/Publication: 2019-02-22 07:00:03 UTC
