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

## Documentation for package ‘weightflow’ version 1.2.0

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

## Help Pages

as_sae_input | Direct estimates and design SEs per domain, ready for small-area estimation
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
data_defect | Data-defect diagnostics for a non-probability sample
design_effect | Kish design effect from unequal weighting
disclosure_risk | Flag re-identification risk from outlier weights within a publication cell
domain_summary | Per-domain weight summary at every stage of the cascade
has_alerts | Quality alerts recorded while preparing a recipe
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
nr_sensitivity | Read the nonresponse-sensitivity analysis from a prepped recipe
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
read_recipe | Read a weighting recipe from a YAML file
reference_sample | Use a weighted survey as the calibration reference instead of a frame
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_nr_sensitivity | Sensitivity of a mean to nonignorable nonresponse or selection
step_pseudoweight | Pseudo-weights for a non-probability sample against a reference
step_rescale | Rescale the weights to a fixed sum
step_round | Round the final weights
step_select_within | Within-cluster selection adjustment
step_subsample | Second-phase subsampling (two-phase sampling)
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
two_phase_variance | Decompose a two-phase variance into V = V1 + V2
weightflow-alerts | Quality alerts raised while preparing a recipe
weightflow-concepts | Conventions shared by every weightflow step
weighting_alerts | Quality alerts recorded while preparing a recipe
weighting_spec | Start a weighting specification
weight_factors | Per-unit adjustment factors table
write_recipe | Write a weighting recipe to a YAML file
y_model | Specify a working model for a study variable y