{% set version = "1.3.4" %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/qgam_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/qgam/qgam_{{ version }}.tar.gz
  sha256: 7633120a48a85ab73f7e1bc8b02c98319285c2abd05f9d13d25339d7aaaacacb

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-doparallel
    - r-mgcv >=1.8_28
    - r-plyr
    - r-shiny
  run:
    - r-base
    - {{ native }}gcc-libs         # [win]
    - r-doparallel
    - r-mgcv >=1.8_28
    - r-plyr
    - r-shiny

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

about:
  home: https://CRAN.R-project.org/package=qgam
  license: GPL-2.0-or-later
  summary: Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017) <arXiv:1707.03307>. Differently from 'quantreg', the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm.
    The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in 'mgcv', but fits non-parametric quantile regression models.
  license_family: GPL2
  license_file:
    - {{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2

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

# Package: qgam
# Type: Package
# Title: Smooth Additive Quantile Regression Models
# Version: 1.3.2
# Date: 2020-02-03
# Authors@R: c(person("Matteo", "Fasiolo", email = "matteo.fasiolo@gmail.com", role = c("aut", "cre")), person("Simon", "N. Wood", role = c("ctb")), person("Margaux", "Zaffran", role = c("ctb")), person("Yannig", "Goude", role = c("ctb")), person("Raphael", "Nedellec", role = c("ctb")))
# Maintainer: Matteo Fasiolo <matteo.fasiolo@gmail.com>
# Description: Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017) <arXiv:1707.03307>. Differently from 'quantreg', the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in 'mgcv', but fits non-parametric quantile regression models.
# License: GPL (>= 2)
# Depends: R (>= 3.5.0), mgcv (>= 1.8-28)
# Imports: shiny, plyr, doParallel, parallel, grDevices
# Suggests: knitr, MASS, RhpcBLASctl, testthat
# VignetteBuilder: knitr
# RoxygenNote: 7.0.2
# NeedsCompilation: yes
# Packaged: 2020-02-03 17:35:37 UTC; teo
# Author: Matteo Fasiolo [aut, cre], Simon N. Wood [ctb], Margaux Zaffran [ctb], Yannig Goude [ctb], Raphael Nedellec [ctb]
# Repository: CRAN
# Date/Publication: 2020-02-03 19:40:02 UTC
