{% set version = '4.4.1' %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/bnlearn_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/bnlearn/bnlearn_{{ version }}.tar.gz
  sha256: 73689fdab937cb5ed5c8ae22607cb3038a161c91030d7e1a1a05145348aa64d9

build:
  merge_build_host: True  # [win]
  number: 1
  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - {{ compiler('c') }}        # [not win]
    - {{ compiler('m2w64_c') }}        # [win]
    - {{posix}}filesystem        # [win]
    - {{posix}}make
    - {{posix}}sed               # [win]
    - {{posix}}coreutils         # [win]
    - {{posix}}zip               # [win]
  host:
    - r-base
  run:
    - r-base
    - {{native}}gcc-libs         # [win]

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

about:
  home: http://www.bnlearn.com/
  license: GPL (>= 2)
  summary: Bayesian network structure learning, parameter learning and inference. This package
    implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC),
    pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and
    hybrid (MMHC and RSMAX2) structure learning algorithms for discrete, Gaussian and
    conditional Gaussian networks, along with many score functions and conditional independence
    tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are
    also implemented. Some utility functions (model comparison and manipulation, random
    data generation, arc orientation testing, simple and advanced plots) are included,
    as well as support for parameter estimation (maximum likelihood and Bayesian) and
    inference, conditional probability queries and cross-validation. Development snapshots
    with the latest bugfixes are available from <http://www.bnlearn.com>.
  license_family: GPL3
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2'

extra:
  recipe-maintainers:
    - conda-forge/r
    - MathiasHaudgaard
    - FrodePedersen
    - ArneKr

# Package: bnlearn
# Type: Package
# Title: Bayesian Network Structure Learning, Parameter Learning and Inference
# Version: 4.4.1
# Date: 2019-03-05
# Depends: R (>= 2.14.0), methods
# Suggests: parallel, graph, Rgraphviz, lattice, gRain, ROCR, Rmpfr, gmp
# Author: Marco Scutari [aut, cre], Robert Ness [ctb]
# Maintainer: Marco Scutari <marco.scutari@gmail.com>
# Description: Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC and RSMAX2) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries and cross-validation. Development snapshots with the latest bugfixes are available from <http://www.bnlearn.com>.
# URL: http://www.bnlearn.com/
# License: GPL (>= 2)
# LazyData: yes
# NeedsCompilation: yes
# Packaged: 2019-03-05 15:57:40 UTC; scutari
# Repository: CRAN
# Date/Publication: 2019-03-05 22:00:03 UTC
