| 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_flows |
Gross-flow TOTALS with standard errors, plus net flows and margins |
| boot_mean |
Bootstrap estimate, standard error and confidence interval |
| boot_total |
Bootstrap estimate, standard error and confidence interval |
| boot_transition |
Transition matrix with per-cell bootstrap standard errors |
| change_estimate |
Net change between two panel waves, with honest variance |
| change_mean |
Net change between two panel waves, with honest variance |
| change_total |
Net change between two panel waves, with honest variance |
| collect_estimates |
Evaluate an estimation pipeline |
| 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 |
| level_estimate |
Level estimate for a single panel wave, with its replicate variance |
| level_mean |
Level estimate for a single panel wave, with its replicate variance |
| level_total |
Level estimate for a single panel wave, with its replicate variance |
| nr_sensitivity |
Read the nonresponse-sensitivity analysis from a prepped recipe |
| panel_cl |
Synthetic rotating- and pure-panel datasets |
| panel_datasets |
Synthetic rotating- and pure-panel datasets |
| panel_design |
Describe the rotating-panel structure of a survey |
| panel_estimate |
Linear combination of panel waves, with honest between-wave variance |
| panel_ine |
Synthetic rotating- and pure-panel datasets |
| panel_mean |
Linear combination of panel waves, with honest between-wave variance |
| panel_merge |
Build the wide longitudinal file from per-wave surveys |
| panel_pr |
Panel-selection probability for a set of combined waves |
| panel_puro |
Synthetic rotating- and pure-panel datasets |
| panel_total |
Linear combination of panel waves, with honest between-wave variance |
| panel_us |
Synthetic rotating- and pure-panel datasets |
| 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_panel |
Panel / longitudinal HTML report |
| 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_attrition |
Attrition adjustment for panel waves |
| step_calibrate |
Calibration to population totals |
| step_cre |
Composite regression estimator (CRE / regression composite estimation) |
| step_cross_sectional |
Declare the recipe's scope: cross-sectional or longitudinal weights |
| step_domain |
Declarative estimation over a coordinated panel object |
| step_drop_ineligible |
Drop ineligible (out-of-scope) units |
| step_estimate |
Declarative estimation over a coordinated panel object |
| step_filter |
Declarative estimation over a coordinated panel object |
| step_longitudinal |
Declare the recipe's scope: cross-sectional or longitudinal weights |
| 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_panel_overlap |
Adjust base weights by the panel-selection probability (CEPAL ch. XVI) |
| 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_transition |
Declarative estimation over a coordinated panel object |
| 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 |
| transition_matrix |
Gross-flow transition matrix between two panel waves |
| two_phase_variance |
Decompose a two-phase variance into V = V1 + V2 |
| wave_bootstrap |
Coordinated bootstrap across panel waves |
| wave_carry |
Extract the carry artifact of a period |
| wave_contrast |
Linear combination of an estimand across a chain of periods |
| wave_jackknife |
Coordinated delete-one jackknife across panel waves |
| wave_step |
One period of a coordinated panel bootstrap, chained from the previous ones |
| 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 |
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