as_svydesign            Export weightflow weights to a survey design
bootstrap_estimate      Bootstrap estimate, standard error and
                        confidence interval
bootstrap_weights       Bootstrap replicate weights that re-apply the
                        recipe
collect_replicate_weights
                        Collect replicate weights into a data frame
                        ready for srvyr
collect_weights         Extract the data with the computed weights
design_effect           Kish design effect from unequal weighting
plot.prepped_weighting_spec
                        Diagnostic plots for the weights
population              Example target population for weightflow
prep                    Estimate the weighting cascade
report_weighting        Build a nice HTML report of the weighting
                        recipe
sample_one              Example survey sample (select-one-person,
                        multistage)
sample_survey           Example survey sample (take-all roster)
step_assert             Assert conditions on the weights at this point
                        of the cascade
step_calibrate          Calibration to population totals
step_drop_ineligible    Drop ineligible (out-of-scope) units
step_model_calibration
                        Model calibration (model-assisted, Wu & Sitter
                        2001)
step_nonresponse        Nonresponse adjustment
step_rescale            Rescale (normalize) the weights
step_round              Round the final weights
step_select_within      Within-household selection adjustment
step_trim               Trim extreme weights
step_trim_weights       Automatic weight trimming (survey-style)
step_unknown_eligibility
                        Unknown-eligibility adjustment
summary.prepped_weighting_spec
                        Detailed per-step diagnostics
weight_factors          Per-unit adjustment factors table
weighting_spec          Start a weighting specification
y_model                 Specify a working model for a study variable y
