Package index • weightflow

Overview

Define the recipe, estimate it, and pull the weights out.

weighting_spec(): Start a weighting specification
prep(): Estimate the weighting cascade
weighting_alerts() has_alerts(): Quality alerts recorded while preparing a recipe
collect_weights(): Extract the data with the computed weights
y_model(): Specify a working model for a study variable y
reference_sample(): Use a weighted survey as the calibration reference instead of a frame
write_recipe(): Write a weighting recipe to a YAML file
read_recipe(): Read a weighting recipe from a YAML file

The staged adjustments, applied in the order you pipe them.

step_unknown_eligibility(): Unknown-eligibility adjustment
step_drop_ineligible(): Drop ineligible (out-of-scope) units
step_select_within(): Within-cluster selection adjustment
step_subsample(): Second-phase subsampling (two-phase sampling)
step_nonresponse(): Nonresponse adjustment
step_pseudoweight(): Pseudo-weights for a non-probability sample against a reference
step_calibrate(): Calibration to population totals
step_model_calibration(): Model calibration (Wu and Sitter 2001)
step_trim(): Trim extreme weights against a ratio
step_trim_weights(): Automatic weight trimming to an absolute band
step_trim_calibrated(): Trimmed calibration (range-restricted, totals-preserving)
step_round(): Round the final weights
step_rescale(): Rescale the weights to a fixed sum
step_assert(): Assert quality conditions on the weights
step_nr_sensitivity(): Sensitivity of a mean to nonignorable nonresponse or selection
step_cross_sectional() step_longitudinal(): Declare the recipe's scope: cross-sectional or longitudinal weights
step_panel_overlap(): Adjust base weights by the panel-selection probability (CEPAL ch. XVI)
step_attrition(): Attrition adjustment for panel waves
step_cre(): Composite regression estimator (CRE / regression composite estimation)

Inspect, summarise and report the cascade.

summary(<prepped_weighting_spec>): Detailed per-step diagnostics
plot(<prepped_weighting_spec>): Diagnostic plots for the weights
weight_factors(): Per-unit adjustment factors table
collect_propensities(): Recover the fitted response propensities of a nonresponse step
collect_step_detail(): Per-unit detail of one step of the cascade
domain_summary(): Per-domain weight summary at every stage of the cascade
design_effect(): Kish design effect from unequal weighting
data_defect(): Data-defect diagnostics for a non-probability sample
disclosure_risk(): Flag re-identification risk from outlier weights within a publication cell
nr_sensitivity(): Read the nonresponse-sensitivity analysis from a prepped recipe
report_weighting(): Self-contained HTML quality report for a weighting recipe

Bootstrap and jackknife that re-apply the recipe, plus survey/srvyr bridges.

bootstrap_weights(): Recipe-aware bootstrap replicate weights
two_phase_variance(): Decompose a two-phase variance into V = V1 + V2
bootstrap_estimate() boot_total() boot_mean(): Bootstrap estimate, standard error and confidence interval
jackknife_weights(): Recipe-aware delete-a-PSU jackknife replicate weights
jackknife_estimate() jack_total() jack_mean(): Jackknife estimate, standard error and confidence interval
as_svydesign() as_svrepdesign(): Export weightflow weights to a survey design
collect_replicate_weights(): Collect replicate weights into a data frame ready for srvyr
as_sae_input(): Direct estimates and design SEs per domain, ready for small-area estimation
print(<weightflow_boot>): Print a bootstrap replicate-weight object
print(<weightflow_jack>): Print a jackknife replicate-weight object

Rotating and pure panels – structure, net change, chaining and gross flows.

panel_design(): Describe the rotating-panel structure of a survey
panel_merge(): Build the wide longitudinal file from per-wave surveys
panel_pr(): Panel-selection probability for a set of combined waves
wave_bootstrap(): Coordinated bootstrap across panel waves
wave_jackknife(): Coordinated delete-one jackknife across panel waves
change_estimate() change_mean() change_total(): Net change between two panel waves, with honest variance
level_estimate() level_mean() level_total(): Level estimate for a single panel wave, with its replicate variance
panel_estimate() panel_mean() panel_total(): Linear combination of panel waves, with honest between-wave variance
wave_step(): One period of a coordinated panel bootstrap, chained from the previous ones
wave_carry(): Extract the carry artifact of a period
wave_contrast(): Linear combination of an estimand across a chain of periods
transition_matrix(): Gross-flow transition matrix between two panel waves
boot_transition(): Transition matrix with per-cell bootstrap standard errors
boot_flows(): Gross-flow TOTALS with standard errors, plus net flows and margins
report_panel(): Panel / longitudinal HTML report

Declare domains and estimands once, read them off a saved replicate object.

step_domain() step_filter() step_estimate() step_transition(): Declarative estimation over a coordinated panel object
collect_estimates(): Evaluate an estimation pipeline

population: Synthetic target population (sampling frame)
sample_survey: Synthetic person sample with a take-all household roster
sample_one: Synthetic address sample with one selected person per household
panel_puro panel_cl panel_ine panel_us: Synthetic rotating- and pure-panel datasets