{% set version = "1.8.4-1" %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/emmeans_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/emmeans/emmeans_{{ version }}.tar.gz
  sha256: c60fd90b4730485b003286796323d0bb6bbd4d2a47cacb82fd98949117245789

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

requirements:
  build:
    - {{ posix }}zip               # [win]
    - cross-r-base {{ r_base }}    # [build_platform != target_platform]
  host:
    - r-base
    - r-estimability >=1.4.1
    - r-mvtnorm
    - r-numderiv
    - r-xtable >=1.8_2
  run:
    - r-base
    - r-estimability >=1.4.1
    - r-mvtnorm
    - r-numderiv
    - r-xtable >=1.8_2

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

about:
  home: https://github.com/rvlenth/emmeans
  license: GPL-2.0-or-later
  summary: 'Obtain estimated marginal means (EMMs) for many linear, generalized  linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed,
    and Milliken (1980) Population marginal means  in the linear model: An alternative to least squares means, The American  Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.'
  license_family: GPL3
  license_file:
    - {{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2
    - {{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-3

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

# Package: emmeans
# Type: Package
# Title: Estimated Marginal Means, aka Least-Squares Means
# Version: 1.4.1
# Date: 2019-09-12
# Authors@R: c(person("Russell", "Lenth", role = c("aut", "cre", "cph"),  email = "russell-lenth@uiowa.edu"), person("Henrik", "Singmann", role = "ctb"), person("Jonathon", "Love", role = "ctb"), person("Paul", "Buerkner", role = "ctb"), person("Maxime", "Herve", role = "ctb"))
# Depends: R (>= 3.2)
# Imports: estimability (>= 1.3), graphics, methods, numDeriv, stats, utils, plyr, mvtnorm, xtable (>= 1.8-2)
# Suggests: bayesplot, biglm, brms, car, coda (>= 0.17), ggplot2, lattice, mediation, mgcv, multcomp, multcompView, nlme, ordinal (>= 2014.11-12), pbkrtest (>= 0.4-1), lme4, lmerTest (>= 2.0.32), MASS, MuMIn, rsm, knitr, rmarkdown, scales, testthat
# Enhances: CARBayes, coxme, gee, geepack, MCMCglmm, MCMCpack, mice, nnet, pscl, rstanarm, sommer, survival
# URL: https://github.com/rvlenth/emmeans
# BugReports: https://github.com/rvlenth/emmeans/issues
# LazyData: yes
# ByteCompile: yes
# Description: Obtain estimated marginal means (EMMs) for many linear, generalized  linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means  in the linear model: An alternative to least squares means, The American  Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
# License: GPL-2 | GPL-3
# Encoding: UTF-8
# RoxygenNote: 6.1.1
# VignetteBuilder: knitr
# NeedsCompilation: no
# Packaged: 2019-09-11 22:32:18 UTC; rlenth
# Author: Russell Lenth [aut, cre, cph], Henrik Singmann [ctb], Jonathon Love [ctb], Paul Buerkner [ctb], Maxime Herve [ctb]
# Maintainer: Russell Lenth <russell-lenth@uiowa.edu>
# Repository: CRAN
# Date/Publication: 2019-09-12 05:10:03 UTC