NEWS
DiscreteFDR 2.1.1 (2026-02-13)
- Replaced deprecated functions used by
fast.Discrete() and in the
vignettes.
DiscreteFDR 2.1.0 (2024-12-14)
- 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 (2024-10-24)
- 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 (2024-07-08)
- 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 (2024-02-08)
- 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 (2021-09-03)
- 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 (2021-05-25)
- 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 (2020-02-14)
- 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.