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.