Changes in version 0.1.0 (2026-06-30)                  

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.