{% set version = '2.9-2' %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/mboost_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/mboost/mboost_{{ version }}.tar.gz
  sha256: 34c6ba2051adc5ff429a594f7144bffcb7b129d5ff7c28a14cf21f38dbd554aa

build:
  merge_build_host: True  # [win]
  number: 0
  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
    - r-matrix
    - r-lattice
    - r-nnls
    - r-partykit >=1.2_1
    - r-quadprog
    - r-stabs >=0.5_0
    - r-survival
  run:
    - r-base
    - {{native}}gcc-libs         # [win]
    - r-matrix
    - r-lattice
    - r-nnls
    - r-partykit >=1.2_1
    - r-quadprog
    - r-stabs >=0.5_0
    - r-survival

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

about:
  home: https://github.com/boost-R/mboost
  license: GPL-2
  summary: Functional gradient descent algorithm (boosting) for optimizing general risk functions
    utilizing component-wise (penalised) least squares estimates or regression trees
    as base-learners for fitting generalized linear, additive and interaction models
    to potentially high-dimensional data.
  license_family: GPL2
  license_file:
    - '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2'

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

# Package: mboost
# Title: Model-Based Boosting
# Version: 2.9-2
# Date: 2020-02-17
# Authors@R: c(person("Torsten", "Hothorn", role = "aut", comment = c(ORCID = "0000-0001-8301-0471")), person("Peter", "Buehlmann", role = "aut"), person("Thomas", "Kneib", role = "aut"), person("Matthias", "Schmid", role = "aut"), person("Benjamin", "Hofner", role = c("aut", "cre"), email = "benjamin.hofner@pei.de", comment = c(ORCID = "0000-0003-2810-3186")), person("Fabian", "Sobotka", role = "ctb"), person("Fabian", "Scheipl", role = "ctb"), person("Andreas", "Mayr", role = "ctb"))
# Description: Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.
# Depends: R (>= 3.2.0), methods, stats, parallel, stabs (>= 0.5-0)
# Imports: Matrix, survival, splines, lattice, nnls, quadprog, utils, graphics, grDevices, partykit (>= 1.2-1)
# Suggests: TH.data, MASS, fields, BayesX, gbm, mlbench, RColorBrewer, rpart (>= 4.0-3), randomForest, nnet, testthat (>= 0.10.0), kangar00
# LazyData: yes
# License: GPL-2
# BugReports: https://github.com/boost-R/mboost/issues
# URL: https://github.com/boost-R/mboost
# NeedsCompilation: yes
# Packaged: 2020-02-17 21:24:00 UTC; benjamin
# Author: Torsten Hothorn [aut] (<https://orcid.org/0000-0001-8301-0471>), Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut, cre] (<https://orcid.org/0000-0003-2810-3186>), Fabian Sobotka [ctb], Fabian Scheipl [ctb], Andreas Mayr [ctb]
# Maintainer: Benjamin Hofner <benjamin.hofner@pei.de>
# Repository: CRAN
# Date/Publication: 2020-02-18 08:20:02 UTC
