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.