{% set version = "1.18.0" %}
{% set name = "LedPred" %}
{% set bioc = "3.9" %}

package:
  name: 'bioconductor-{{ name|lower }}'
  version: '{{ version }}'
source:
  url:
    - 'https://bioconductor.org/packages/{{ bioc }}/bioc/src/contrib/{{ name }}_{{ version }}.tar.gz'
    - 'https://bioarchive.galaxyproject.org/{{ name }}_{{ version }}.tar.gz'
    - 'https://depot.galaxyproject.org/software/bioconductor-{{ name|lower }}/bioconductor-{{ name|lower }}_{{ version }}_src_all.tar.gz'
  md5: a8c110446d86b622593f1326c4757d3c
build:
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/
  noarch: generic
requirements:
  host:
    - r-akima
    - r-base
    - 'r-e1071 >=1.6'
    - r-ggplot2
    - r-irr
    - r-jsonlite
    - r-plot3d
    - r-plyr
    - r-rcurl
    - r-rocr
    - r-testthat
  run:
    - r-akima
    - r-base
    - 'r-e1071 >=1.6'
    - r-ggplot2
    - r-irr
    - r-jsonlite
    - r-plot3d
    - r-plyr
    - r-rcurl
    - r-rocr
    - r-testthat
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: 'MIT | file LICENSE'
  summary: 'This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences.'

