NEWS

betaNB 1.0.7.9001

DeltaRSqNB() gains adj = TRUE for differences in adjusted R-squared. confint() and summary() gain type = "mpc" for modified percentile intervals of Delta R-squared (sr2), unadjusted or adjusted. The sample F test uses the interval's alpha and the unadjusted increment. Upper limits and ordinary percentile interpolation are unchanged. Existing defaults and "pc", "bc", "bca" intervals are unchanged.

NB() gains the wild bootstrap: type = "wild" (default "pairs", unchanged), with weights ("rademacher", "mammen", "webb", "normal"), hc ("hc3", "hc0", "hc1", "hc2", "hc4", "hc4m", "hc5"; the same leverage adjustments as betaSandwich), and x ("random" resamples the regressors with their residuals, "fixed" keeps them). The effect size functions and intervals work on the wild draws unchanged. The sampler is in R for now.
New FSqNB(): Cohen's f-squared and the local f-squared of each regressor.
Faster bootstrap: the bootstrap and jackknife covariance matrices (closed-form leave-one-out) and the effect sizes are computed in C++ (Rcpp, RcppArmadillo), once per NB() call. The bootstrap samples are the same as before for the same seed.
The thetahatstar and jackknife elements of nb and betanb objects are now matrices (one draw per row) instead of lists. nb objects also hold es and es_jackknife.
Selective standardization: the arguments std_y and std_x standardize only y or only the named regressors (for example, leave a dummy variable in its 0-1 units). The defaults give the same results as before.