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discrete.GR(), discrete.LR(), discrete.PB() and their respective
wrappers DGR(), DLR(), DPB(), NDGR() , NDLR() and NDPB() are
now generic functions. The previously existing functionality is
implemented in *.default methods.
These generic functions got *.DiscreteTestResults methods for
processing DiscreteTestResults R6 class objects from package DiscreteTests directly, so they can be used within pipes. They also
offer some performance gains because some time-consuming checks for
consistency are no longer necessary.
For consistency of new generics and methods, the first parameter raw.pvalues needed to be renamed to test.results.
New parameter threshold for discrete.*(), continuous.*(), weighted.* and their respective wrapper functions. 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,
they now respect the re-scaled conditional distributions. Additionally,
the FDX S3 class output objects of these functions now include a list Select with values and information regarding selection.
New parameter pCDFlist.indices for discrete.*() and their wrappers,
which must have the same length as pCDFlist and may help increasing
performance considerably. As pCDFlist may now include only unique
supports, pCDFlist.indices must indicate the indices of the p-values
which belong to a given support set. 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 direct.discrete.GR(), direct.discrete.LR() and direct.discrete.PB() as more flexible replacements for fast.discrete.GR(), fast.discrete.LR() and fast.discrete.PB(). The
latter have been marked as deprecated and will be removed in the future.
Step function evaluation in C++ code has been replaced by closely optimized
inline functions which offer performance gains of 10-50%.