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.