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DiscreteFDR 2.1.0

• Added DBY() for discrete Benjamini-Yekutieli procedure.
• If input p-values vector includes names, they are now included in the summary table generated by summary.DiscreteFDR(). For this to work with DiscreteTestResults class objects from package DiscreteTests, version 0.2.1 of that package is required.
• Minor fix for wrong p-value CDF indices after selection. For the way they are used, this was inconsequential, but may have caused problems in the future.
• Change order of output data: Data list is now output before Select list.
• Fixed issues with Rcpp’s rev() function in computations of adaptive DBH critical values

DiscreteFDR 2.0.1

• Introduction of mode parameter for hist() function to adapt construction of histograms in case of conditional p-value selection.
• Remove amnesia dataset (moved to DiscreteDatasets package).
• Function match.pvals() is no longer exported.
• Performance improvement for step-up procedures, especially for large numbers of tests.

DiscreteFDR 2.0.0

• New features:
  • discrete.BH(), DBH(), ADBH() and DBR() are now generic functions. The previously existing functionality is implemented in *.default methods.
  • discrete.BH(), DBH(), ADBH() and DBR() got *.DiscreteTestResults methods for processing DiscreteTestResults R6 class objects from package *.DiscreteTests directly, so they can be used within pipes.
  • For consistency of new generics and methods, the first parameter raw.pvalues needed to be renamed to test.results.
  • New parameter threshold for discrete.BH(), DBH(), ADBH() and DBR(). This enables selection of p-values which are smaller than or equal to a certain value. Note: observed p-values and their supports are then re-scaled, as the p-value distributions are now becoming conditional distributions. If no selection is performed (i.e. threshold = 1), print(), summary() and plot() outputs are as before. Otherwise, the now respect the re-scaled conditional distributions. Additionally, the DiscreteFDR S3 class output objects of the functions discrete.BH(), DBH(), ADBH() and DBR() now include a list Select with values and information regarding selection.
  • New parameter pCDFlist.indices for discrete.BH(), DBH(), ADBH() and DBR(), which must have the same length as pCDFlist and may help increasing performance considerably. As pCDFlist can now include only unique supports, pCDFlist.indices must indicate the index of the p-values to which a given support belongs. If pCDFlist has the same length as test.results, it can be omitted (by setting it to NULL, the default). If users prefer using DiscreteTestResults objects, they do not have to take care of this, as unique supports and indices are automatically extracted from these objects.
• New functions generate.pvalues() and direct.discrete.BH() as more flexible replacements for fisher.pvalues.support() and fast.discrete().
• Step function evaluation in C++ code has been replaced by closely optimized inline functions which offer performance gains of 10-50%.

DiscreteFDR 1.3.7

• Introduction of lifecycle mechanisms.
• Marked fast.Discrete(), fisher.pvalues.support(), match.pvals(), kernel_*() and amnesia dataset as deprecated.
• Various documentation updates.
• Removal of links to discreteMTP packages, since it was removed from CRAN.

DiscreteFDR 1.3.6

• Fixed a problem with fisher.pvalues.support that could cause p-values to be wrong or NA (Thanks to Iqraa Meah).
• Added GitHub.

DiscreteFDR 1.3.5

• Fixed a problem with fisher.pvalues.support that could cause an infinite loop when using alternative = two.sided (Thanks to Lukas Jansen).
• Changed version scheme from x.y-z to x.y.z

DiscreteFDR 1.3-4

• Added a NEWS.md file to track changes to the package.
• Corrected a bug in plot.DiscreteFDR function that produced a false legend.
• Added plausibility checks of arguments to discrete.BH and DBR functions.

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