{% set version = '1.0-7' %}

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

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

source:
  fn: ROCR_{{ version }}.tar.gz
  url:
    - https://cran.r-project.org/src/contrib/ROCR_{{ version }}.tar.gz
    - https://cran.r-project.org/src/contrib/Archive/ROCR/ROCR_{{ version }}.tar.gz
  sha256: e7ef710f847e441a48b20fdc781dbc1377f5a060a5ee635234053f7a2a435ec9

build:
  number: 0
  skip: true  # [win32]
  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - r-base
    - r-gplots
  run:
    - r-base
    - r-gplots

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

about:
  home: http://rocr.bioinf.mpi-sb.mpg.de/
  license: GPL (>= 2)
  summary: ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots
    are popular examples of trade-off visualizations for specific pairs of performance
    measures. ROCR is a flexible tool for creating cutoff-parameterized 2D performance
    curves by freely combining two from over 25 performance measures (new performance
    measures can be added using a standard interface). Curves from different cross-validation
    or bootstrapping runs can be averaged by different methods, and standard deviations,
    standard errors or box plots can be used to visualize the variability across the
    runs. The parameterization can be visualized by printing cutoff values at the corresponding
    curve positions, or by coloring the curve according to cutoff. All components of
    a performance plot can be quickly adjusted using a flexible parameter dispatching
    mechanism. Despite its flexibility, ROCR is easy to use, with only three commands
    and reasonable default values for all optional parameters.
  license_family: GPL3

extra:
  recipe-maintainers:
    - johanneskoester
    - bgruening
