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First release.
A dependency-free, pipeable API to compute survey weights from design
base weights through a chain of hierarchical adjustment stages. Build a
recipe lazily, estimate it with prep(), and extract the
weights with collect_weights().
step_unknown_eligibility() — redistribute the weight of
unknown-eligibility cases to the known ones (person- or household-level
via cluster).step_drop_ineligible() — zero out out-of-scope
units.step_select_within() — within-household selection
(unequal prob or equal n_eligible).step_nonresponse() — weighting-class or propensity
adjustment, at the person or household level
(cluster).step_calibrate() — raking, post-stratification and
linear/GREG calibration, with bounded (Deville-Särndal) and integrative
cluster options.step_model_calibration() — Wu-Sitter model
calibration.step_trim(), step_trim_weights(),
step_round(), step_rescale() — trimming,
rounding and rescaling.step_assert() — quality checkpoint (deff, weight ratio,
effective n).summary(), plot() and
weight_factors() for per-stage diagnostics.design_effect() for the Kish design effect and
effective sample size.report_weighting() builds a self-contained HTML report
with a pipeline diagram, the variables used, per-stage summaries and
per-step visuals.population,
sample_survey (take-all roster) and sample_one
(multistage select-one design).This package produces weights only; for variance estimation, export
the final weights to the survey package.
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.