{% set version = '1.1-7.4' %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/DPpackage_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/DPpackage/DPpackage_{{ version }}.tar.gz
  sha256: a6a9a8d1a85873a4dbe5894d376f811630731122e6a2d4266e2173b96629dc51

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

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

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

about:
  home: http://www.mat.puc.cl/~ajara
  license: GPL (>= 2)
  summary: Functions to perform inference via simulation from the posterior distributions for
    Bayesian nonparametric and semiparametric models. Although the name of the package
    was motivated by the Dirichlet Process prior, the package considers and will consider
    other priors on functional spaces. So far, DPpackage includes models considering
    Dirichlet Processes, Dependent Dirichlet Processes, Dependent Poisson- Dirichlet
    Processes, Hierarchical Dirichlet Processes, Polya Trees, Linear Dependent Tailfree
    Processes, Mixtures of Triangular distributions, Random Bernstein polynomials priors
    and Dependent Bernstein Polynomials.  The package also includes models considering
    Penalized B-Splines.  Includes semiparametric models for marginal and conditional
    density estimation, ROC curve analysis, interval censored data, binary regression
    models, generalized linear mixed models, IRT type models, and generalized additive
    models. Also contains functions to compute Pseudo-Bayes factors for model comparison,
    and to elicitate the precision parameter of the Dirichlet Process. To maximize computational
    efficiency, the actual sampling for each model is done in compiled FORTRAN. The
    functions return objects which can be subsequently analyzed with functions provided
    in the 'coda' package.
  license_family: GPL3
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2'

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

# Package: DPpackage
# Version: 1.1-7.4
# Date: 2018-01-05
# Title: Bayesian Nonparametric Modeling in R
# Authors@R: c(person("Alejandro", "Jara", role = c("aut", "cre"), email = "atjara@uc.cl"), person("Timothy", "Hanson", role = "ctb", email = "hansont@stat.sc.edu"), person("Fernando", "Quintana", role = "ctb", email = "quintana@mat.puc.cl"), person("Peter", "Mueller", role = "ctb", email = "pmueller@math.utexas.edu"), person("Gary", "Rosner", role = "ctb", email = "grosner@jhmi.edu"))
# Depends: R (>= 2.10)
# Imports: MASS, nlme, survival, splines, methods
# Description: Functions to perform inference via simulation from the posterior distributions for Bayesian nonparametric and semiparametric models. Although the name of the package was motivated by the Dirichlet Process prior, the package considers and will consider other priors on functional spaces. So far, DPpackage includes models considering Dirichlet Processes, Dependent Dirichlet Processes, Dependent Poisson- Dirichlet Processes, Hierarchical Dirichlet Processes, Polya Trees, Linear Dependent Tailfree Processes, Mixtures of Triangular distributions, Random Bernstein polynomials priors and Dependent Bernstein Polynomials.  The package also includes models considering Penalized B-Splines.  Includes semiparametric models for marginal and conditional density estimation, ROC curve analysis, interval censored data, binary regression models, generalized linear mixed models, IRT type models, and generalized additive models. Also contains functions to compute Pseudo-Bayes factors for model comparison, and to elicitate the precision parameter of the Dirichlet Process. To maximize computational efficiency, the actual sampling for each model is done in compiled FORTRAN. The functions return objects which can be subsequently analyzed with functions provided in the 'coda' package.
# License: GPL (>= 2)
# URL: http://www.mat.puc.cl/~ajara
# Author: Alejandro Jara [aut, cre], Timothy Hanson [ctb], Fernando Quintana [ctb], Peter Mueller [ctb], Gary Rosner [ctb]
# Maintainer: ORPHANED
# NeedsCompilation: yes
# Packaged: 2018-01-05 10:55:14 UTC; ripley
# X-CRAN-Original-Maintainer: Alejandro Jara <atjara@uc.cl>
# X-CRAN-Comment: Orphaned and corrected on 2017-09-15 as errors were not corrected despite repeated reminders.
# Repository: CRAN
# Date/Publication: 2018-01-06 08:39:08 UTC
