r-hdrcde

Summary

Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate, and multimodal regression.

Versions

  • 3.1

License

GPL (>= 2)

Meta

package:
  name: r-hdrcde
  # Note that conda versions cannot contain -, so any -'s in the version have
  # been replaced with _'s.
  version: "3.1"

source:
  fn: hdrcde_3.1.tar.gz
  url:
    - http://cran.r-project.org/src/contrib/hdrcde_3.1.tar.gz
    - http://cran.r-project.org/src/contrib/Archive/hdrcde/hdrcde_3.1.tar.gz


  # You can add a hash for the file here, like md5 or sha1
  # md5: 49448ba4863157652311cc5ea4fea3ea
  # sha1: 3bcfbee008276084cbb37a2b453963c61176a322
  # patches:
   # List any patch files here
   # - fix.patch

build:
  # If this is a new build for the same version, increment the build
  # number. If you do not include this key, it defaults to 0.
  # number: 1

  # This is required to make R link correctly on Linux.
  rpaths:
    - lib/R/lib/
    - lib/


requirements:
  build:
    - r
    - r-kernsmooth # [not win]
    - r-ash
    - r-ks
    - r-locfit
    - r-mvtnorm

  run:
    - r
    - r-kernsmooth # [not win]
    - r-ash
    - r-ks
    - r-locfit
    - r-mvtnorm

test:
  commands:
    # You can put additional test commands to be run here.
    - $R -e "library('hdrcde')" # [not win]
    - "\"%R%\" -e \"library('hdrcde')\"" # [win]

  # You can also put a file called run_test.py, run_test.sh, or run_test.bat
  # in the recipe that will be run at test time.

  # requires:
    # Put any additional test requirements here.

about:
  home: !!python/unicode 'http://www.robjhyndman.com/software/hdrcde'

  license: GPL (>= 2)
  summary: !!python/unicode 'Computation of highest density regions in one and two dimensions,
    kernel estimation of univariate density functions conditional on one covariate,
    and multimodal regression.'


# The original CRAN metadata for this package was:

# Package: hdrcde
# Type: Package
# Title: Highest density regions and conditional density estimation
# Version: 3.1
# Date: 2013-10-19
# Author: Rob J Hyndman <Rob.Hyndman@monash.edu> with contributions from Jochen Einbeck and Matt Wand
# Maintainer: Rob J Hyndman <Rob.Hyndman@monash.edu>
# Depends: R (>= 2.15), mvtnorm
# Imports: locfit, ash, ks, KernSmooth
# LazyData: yes
# LazyLoad: yes
# Description: Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate, and multimodal regression.
# License: GPL (>= 2)
# URL: http://www.robjhyndman.com/software/hdrcde
# Packaged: 2013-10-19 12:01:16 UTC; hyndman
# NeedsCompilation: yes
# Repository: CRAN
# Date/Publication: 2013-10-19 15:53:16

# See
# http://docs.continuum.io/conda/build.html for
# more information about meta.yaml