weightflow 0.1.0

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().

Adjustment steps

• 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).

Inspection and reporting

• 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.

Data

• Bundled example datasets 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.