Package: weightflow 0.1.0

Juan Pablo Ferreira

weightflow: Declarative API for Staged Survey Weights

Builds survey weights from design base weights by chaining hierarchical adjustments (unknown eligibility, nonresponse and calibration) through a declarative, pipeable, 'tidymodels'-style API. Calibration follows Deville and Sarndal (1992) <doi:10.2307/2290268>. Variances are obtained with a bootstrap that resamples primary sampling units and re-applies the whole recipe on each replicate, following the rescaling bootstrap of Rao and Wu (1988) <doi:10.1080/01621459.1988.10478591>, so the replicate weights carry the variability of every adjustment. The weights also bridge to the 'survey' and 'srvyr' packages for design-based inference.

Authors:Juan Pablo Ferreira [aut, cre]

weightflow_0.1.0.tar.gz
weightflow_0.1.0.tar.gz(r-4.7-any)weightflow_0.1.0.tar.gz(r-4.6-any)
weightflow_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
weightflow/json (API)

# Install 'weightflow' in R:

install.packages('weightflow', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jpferreira33/weightflow/issues

Pkgdown/docs site:https://jpferreira33.github.io

Datasets:

population - Example target population for weightflow
sample_one - Example survey sample
sample_survey - Example survey sample

On CRAN:

2.74 score 11 scripts 25 exports 0 dependencies

Last updated from:9147113b57. Checks:4 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	137
source / vignettes	OK	174
linux-release-x86_64	OK	138
wasm-release	OK	130

Exports:as_svrepdesign as_svydesign boot_mean boot_total bootstrap_estimate bootstrap_weights collect_replicate_weights collect_weights design_effect prep report_weighting step_assert step_calibrate step_drop_ineligible step_model_calibration step_nonresponse step_rescale step_round step_select_within step_trim step_trim_weights step_unknown_eligibility weight_factors weighting_spec y_model

Dependencies:

Variance estimation

Rendered fromvariance-estimation.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2026-06-30

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

Package: weightflow 0.1.0

weightflow: Declarative API for Staged Survey Weights

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)