weightflow - Weighting Probability and Non-Probability Survey Samples with Recipe-Aware Replicate Variances

Builds survey analysis weights by declaring the whole weighting process as an ordered recipe of explicit adjustments, estimated in a single call: unknown eligibility, within-cluster selection, two-phase subsampling, nonresponse by weighting classes or response-propensity models, calibration and trimming. Calibration to known totals runs on linear, raking or logit distances, bounded, ridge-penalized, model-assisted or integrative with one weight per cluster; a calibrated trim bounds the weights while keeping the control totals exact. Non-probability samples are weighted against a probability reference sample by pseudo-weighting, mass imputation and doubly robust estimators that calibrate a single weight on the fitted values of several working models and on the population totals of the auxiliaries at once, so one multipurpose weight serves many variables. Variances come from a recipe-aware bootstrap and jackknife that re-apply the entire cascade on each replicate, so every adjustment is inside the variance instead of being treated as fixed; across panel waves the replicates are coordinated by carrying forward the selection multiplicities of the sampling units, so the sample overlap enters the variance of a net change as covariance. Rotating and pure panels add attrition, longitudinal weights, gross flows and composite estimation, a self-contained HTML report documents each step, and the weights bridge to the 'survey' and 'srvyr' packages, which are optional: the package itself runs on base R. The methods, and the simulation evidence behind the variance estimators, are described in Ferreira (2026) <doi:10.1177/18747655261484262>.