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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.
Health stats visible at Monitor.