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perm_cont():
X_star is a genuine permutation of
X (rather than a re-sample) and preserves X’s
empirical marginal distribution. Because this changes both the RNG
stream and the null distribution, permutation p-values from
cit_perm(),cit_multi() and
cit_gsa() will differ from previous versions. Of note,
previous scheme could be anti-conservative, and some limiting cases were
badly handled..lm.fit() to be
robust to badly scaled covariates (eg library sizes).sd(fit) * n^(-1/3)) rather than
previous 1/(difference in fitted values)^2 (each
observation effectively drew from its two nearest neighbours whatever
the sample size, leading to bad calibration of the test with too many
false positives). This also changes the null distribution.space_y = TRUE now uses the same y-threshold grid
across all tests: cit_asymp(), and cit_gsa()
adopt cit_perm()’s upper endpoint max(Y)
instead of the second-largest observation. Asymptotic results will be
different than previously with defaults, although calibration remains
unchanged.
similarly, ccdf() now uses the same y-threshold grid
as the tests when space_y = TRUE (starting at the smallest
non-zero observation rather than the second smallest). CCDF values from
ccdf(space_y = TRUE) therefore differ from previous
versions (the default space_y = FALSE is unaffected). It
also no longer errors when all Y are equal.
first CRAN release
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.