Changelog

Source: NEWS.md

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(). Separating define from apply makes the whole process reproducible and auditable, and lets the bootstrap re-run the entire cascade on each replicate.

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 (one weight per household) 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.

Variance estimation

• bootstrap_weights() resamples PSUs within strata (Rao-Wu rescaling) and re-applies the whole recipe on each replicate, so the replicate weights carry the variability of every adjustment.
• boot_mean() and boot_total() return the estimate, standard error and CI.
• as_svydesign(), as_svrepdesign() and collect_replicate_weights() bridge to the survey and srvyr packages for design-based inference.

Data

• Bundled example datasets population, sample_survey (take-all roster) and sample_one (multistage select-one design), all with stratum, PSU and design weight.

Development version

The following are available in the development version on GitHub and are planned for a future CRAN release:

• Machine-learning response propensities (CART, random forest and gradient boosting via xgboost) for step_nonresponse() and step_model_calibration().
• k-fold cross-fitting (crossfit) to estimate each unit out-of-sample, with folds formed by cluster to avoid leakage.
• Ridge (penalized) calibration (penalty) to keep weights stable with many auxiliaries.
• Potter MSE-optimal trimming (method = "potter"), a data-driven cutoff.

Install with remotes::install_github("jpferreira33/weightflow") to use them today.