citcdf 1.1.0

• perm_cont():

  • now draws without replacement, so the conditional permutation 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.
  • now uses QR decomposition through .lm.fit() to be robust to badly scaled covariates (eg library sizes).
  • now weights draws with a Gaussian kernel on the fitted values (bandwidth 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