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positivity_trim argument to ROOT()
and characterizing_underrep() for pre-trimming target
observations with structural positivity violations prior to
optimization. Supports a fixed numeric threshold (e.g.,
positivity_trim = 0.05) or the data-driven rule of Crump,
Hotz, Imbens & Mitnik (2009, Biometrika) via
positivity_trim = "crump". Only active in generalizability
mode. Trimming metadata is stored in the returned object as
positivity_trim_info and reported by
summary().split_strategy argument to ROOT()
and characterizing_underrep() with options
"midpoint" (default, backward-compatible) and
"random_quantile". The random quantile strategy draws a
uniform random quantile within quantile_range (default
10th–90th percentile) at each split, improving covariate-space coverage
across the forest. The strategy used is stored in the returned object
and reported by summary().crump_alpha(),
trim_positivity_violations(), and
split_point().@references in
ROOT().summary.ROOT() now reports the split strategy and
positivity trimming diagnostics.test-positivity.R and
test-split-strategy.R.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.
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