Changelog
Source:NEWS.md
fwb
(development version)
Fixed a bug in
print.fwbci()
due to incorrect ordering of the intervals which led them to be printed with incorrect labels. These have been corrected and printing is a little prettier. Thanks to Katya Zelevinsky.Documentation and vignette updates.
fwb
0.3.0
CRAN release: 2025-03-03
Added a new
confint()
method forfwb
objects.Added a new
strata
argument tofwb()
to perform stratified bootstrapping within levels of a stratification variable.Added a new
drop0
argument tofwb()
to drop all units with weights of 0 in each bootstrap iteration.Added a new
.coef
argument tovcovFWB()
. A function can be supplied to extract a vector of coefficients from the fitted model in each bootstrap iteration if the default (stats::coef()
) doesn’t return a numeric vector (e.g., fornnet::multinom()
models). An error message is now thrown if.coef
doesn’t return a numeric vector.Added support for using
future
backend forfwb()
by supplyingcl = "future"
. Thanks to Katya Zelevinsky for the suggestion.Added a new vignette on reproducibility and parallelization, which can be accessed at
vignette("fwb-rep")
.For
fwb()
,simple
has a new default that isTRUE
in most cases andFALSE
whenwtype
is"multinom"
. This should not affect results but will reduce memory use for large datasets by avoiding computing all bootstrap weights simultaneously. Note that when there is randomness in thestatistic
supplied tofwb()
, the argument tosimple
affects whether BCa confidence intervals can be computed. See the reproducibility vignette mentioned above for details.A warning is now thrown when using
fwb()
withsimple = TRUE
with non-NULL
cl
when the random number generator kind is not"L'Ecuyer-CMRG"
. Under these circumstances, results may not replicate and the BCa confidence interval will be inaccurate. See the reproducibility vignette mentioned above for details.Fixed a bug where the names of quantities produced by
fwb()
whenstatistic
returns an unnamed vector were incorrect.When BCa confidence intervals are requested, an error is thrown if the number of bootstrap replications is smaller than the sample size.
Documentation updates.
fwb
0.2.0
CRAN release: 2023-12-07
fwb()
andvcovFWB()
now take an additional argument,wtype
, which specifies how the weights are drawn. The default,"exp"
is still to draw weights from an distribution but other options, namely"multinom"
for multinomial integer weights (which reproduceboot::boot()
results exactly),"poisson"
for Poisson integer weights, and"mammen"
for second-order accurate Mammen weights as recommended by Lihua Lei here. (#4)New functions
set_fwb_wtype()
andget_fwb_wtype()
allow one to set global defaults for thewtype
argument offwb()
and vcovFWB()`.