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create_ps_weights() and
create_ps_matched_cohort() now also emit the unified
PS-distribution figures (density, within-group histogram, and a
histogram+density overlay for the unweighted/pre and weighted/post
samples, plus a weight box plot for the weighting methods) that
create_ps_fs_weights() already produced. The existing
faceted / mirror plots are still written. A new internal helper
.plot_ps_distribution_set() is shared by all three
functions, so the figure style is now consistent across PS methods.
create_ps_fs_weights() figure output is unchanged.create_ps_matched_cohort() gains a
caliper_scale argument controlling the scale on which
matching and the caliper are applied: "logit_ps_sd" matches
on logit(PS) with a caliper of caliper x SD(logit(PS))
(Austin 2011); "raw_ps_sd" matches on PS with a caliper of
caliper x SD(PS); "raw" applies a flat caliper
on the raw PS scale (e.g. caliper = 0.01).caliper x SD(PS)); the new default
(caliper_scale = "logit_ps_sd") matches on logit(PS)
(caliper x SD(logit(PS))). Pass
caliper_scale = "raw_ps_sd" to reproduce the previous
results exactly. The verbose/report label that previously described the
raw-PS caliper as “SD of logit(PS)” is now accurate for each scale.'Excel' per CRAN
software-name convention.Description
field (Rosenbaum and Rubin 1983 doi:10.1093/biomet/70.1.41; Austin 2011 doi:10.1080/00273171.2011.568786; Desai et al. 2017 doi:10.1097/EDE.0000000000000595).estimate_rr_rd(): removed hardcoded RNG seed from the
internal bootstrap helper. New seed argument (default
NULL) lets the user opt in to reproducibility; when
NULL the function does not touch the global RNG state. In
the sequential branch, .Random.seed is saved and restored
on exit when a seed is supplied.expect_message(..., "Auto-excluding.*race_cat"), which
required both substrings in a single message() call, but
the verbose path emits the header and the variable name on separate
lines. Switched to capture_messages() and asserts both
substrings individually.estimate_ps() categorical extreme-distribution
screen now detects zero-count levels via union of levels across exposure
groups. Previously, a level present in only one exposure group was
silently dropped by table() and could slip past the
cnt < 5 rule, triggering quasi-separation in
glm().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.