r-dunn.test

Summary

Computes Dunn’s test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. ‘dunn.test’ makes k(k-1)/2 multiple pairwise comparisons based on Dunn’s z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn’s test may be understood as a test for median difference. ‘dunn.test’ accounts for tied ranks.

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Versions

  • 1.3.1

License

GPL-2

Meta

package:
  name: r-dunn.test
  # Note that conda versions cannot contain -, so any -'s in the version have
  # been replaced with _'s.
  version: "1.3.1"

source:
  fn: dunn.test_1.3.1.tar.gz
  url:
    - http://cran.r-project.org/src/contrib/dunn.test_1.3.1.tar.gz
    - http://cran.r-project.org/src/contrib/Archive/dunn.test/dunn.test_1.3.1.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

  run:
    - r

test:
  commands:
    # You can put additional test commands to be run here.
    - $R -e "library('dunn.test')" # [not win]
    - "\"%R%\" -e \"library('dunn.test')\"" # [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:
  license: GPL-2
  summary: Computes Dunn's test (1964) for stochastic dominance and reports the results among
    multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance
    among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance
    requires an assumption that the CDF of one group does not cross the CDF of the other.
    'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic
    approximations to the actual rank statistics. The null hypothesis for each pairwise
    comparison is that the probability of observing a randomly selected value from the
    first group that is larger than a randomly selected value from the second group
    equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney
    rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous,
    and the distributions are assumed identical except for a difference in location,
    Dunn's test may be understood as a test for median difference. 'dunn.test' accounts
    for tied ranks.

# The original CRAN metadata for this package was:

# Package: dunn.test
# Version: 1.3.1
# Date: 2015-10-08
# Title: Dunn's Test of Multiple Comparisons Using Rank Sums
# Author: Alexis Dinno <alexis.dinno@pdx.edu>
# Maintainer: Alexis Dinno <alexis.dinno@pdx.edu>
# Description: Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. 'dunn.test' accounts for tied ranks.
# License: GPL-2
# LazyData: no
# Encoding: UTF-8
# NeedsCompilation: no
# Packaged: 2015-10-08 15:16:34 UTC; Alexis
# Repository: CRAN
# Date/Publication: 2015-10-08 20:13:40

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