Users can now supply a list of vcov arguments to compare multiple SE adjustments on the fly. (#65)
est = est = feols(flipper_len ~ bill_len | species, penguins)
ggcoefplot(est, vcov = list("iid", "hc1", ~species))keep and drop arguments now work
correctly with a list of models. Thanks to @femdias for the report. (#60)ggcoefplot or ggiplot then the original order
is preserved for grouping and facet behaviour (#63)ggplot2 dependency to v4.0.0 and update test
snapshots. (#55, #59)fixest dependency to v0.13.0 and update tests.
(#58, #59)aggr_es(..., period = "diff") convenience
keyword argument allows users to estimate the difference between the
(mean) post- and pre-treatment periods. Thanks to @FBrunamonti for the suggestion.
(#52).svglite dependency version and update test
snapshots. (#51)aggr_es objects.
(#43)ggh4x dependency with legendry.
(#41 @teunbrand)First CRAN release!
aggr_es function now supports numeric sequences for
aggregating a specific subset of periods, in addition to the existing
keyword strings like “pre” or “post”. This functionality also passes
through to the higher order plotting functions that call
aggr_es under the hood. For example,
ggiplot(est, aggr_eff = 6:8). (#33)ggcoefplot(est, vcov = "hc1"). These on-the-fly
adjustments are done via summary.fixest, and so the effect
is just the same as passing an adjusted object directly, e.g.
ggcoefplot(summary(est, vcov = "hc1")). However, it may
prove more convenient for simultaneously adjusting a list of multiple
models, e.g.
ggcoefplot(list(est1, est2, est3), vcov = "hc1").
(#35)ggcoefplot, a ggplot equivalent of
coefplot (#28).pt.size argument for controlling the size of
point markers (#27). Thanks @jcvdav.keep and drop arguments for
subsetting coefficients (#22).iplot() (#e5cf0b0).log(y (#20).marginaleffects::hypotheses() internally for
aggr_es() to match the upstream changes in
marginaleffects.NEWS.md file to track changes to the
package.