R: Declarative Recipes for Staged Survey Weighting with Recipe-Aware Replicate Variances

# Declarative Recipes for Staged Survey Weighting with Recipe-Aware Replicate Variances

## Documentation for package ‘weightflow’ version 1.1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

as_svrepdesign | Export weightflow weights to a survey design
as_svydesign | Export weightflow weights to a survey design
bootstrap_estimate | Bootstrap estimate, standard error and confidence interval
bootstrap_weights | Recipe-aware bootstrap replicate weights
boot_mean | Bootstrap estimate, standard error and confidence interval
boot_total | Bootstrap estimate, standard error and confidence interval
collect_propensities | Recover the fitted response propensities of a nonresponse step
collect_replicate_weights | Collect replicate weights into a data frame ready for srvyr
collect_step_detail | Per-unit detail of one step of the cascade
collect_weights | Extract the data with the computed weights
design_effect | Kish design effect from unequal weighting
domain_summary | Per-domain weight summary at every stage of the cascade
jackknife_estimate | Jackknife estimate, standard error and confidence interval
jackknife_weights | Recipe-aware delete-a-PSU jackknife replicate weights
jack_mean | Jackknife estimate, standard error and confidence interval
jack_total | Jackknife estimate, standard error and confidence interval
plot.prepped_weighting_spec | Diagnostic plots for the weights
population | Synthetic target population (sampling frame)
prep | Estimate the weighting cascade
print.weightflow_boot | Print a bootstrap replicate-weight object
print.weightflow_jack | Print a jackknife replicate-weight object
report_weighting | Self-contained HTML quality report for a weighting recipe
sample_one | Synthetic address sample with one selected person per household
sample_survey | Synthetic person sample with a take-all household roster
step_assert | Assert quality conditions on the weights
step_calibrate | Calibration to population totals
step_drop_ineligible | Drop ineligible (out-of-scope) units
step_model_calibration | Model-assisted calibration (Wu and Sitter 2001)
step_nonresponse | Nonresponse adjustment
step_rescale | Rescale the weights to a fixed sum
step_round | Round the final weights
step_select_within | Within-cluster selection adjustment
step_trim | Trim extreme weights against a ratio
step_trim_calibrated | Trimmed calibration (range-restricted, totals-preserving)
step_trim_weights | Automatic weight trimming to an absolute band
step_unknown_eligibility | Unknown-eligibility adjustment
summary.prepped_weighting_spec | Detailed per-step diagnostics
weightflow-concepts | Conventions shared by every weightflow step
weighting_spec | Start a weighting specification
weight_factors | Per-unit adjustment factors table
y_model | Specify a working model for a study variable y