{% set version = "1.3.5" %}
{% 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: 9240866e2f773cd0ac8a02514871149d2babaa162a49e151eab9591ad42984ea

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

requirements:
  build:
    - {{ compiler('c') }}        # [not win]
    - {{ compiler('cxx') }}      # [not win]
    - {{ compiler('m2w64_c') }}        # [win]
    - {{ compiler('m2w64_cxx') }}        # [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-2.0-only
  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
