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

Builds survey analysis weights by declaring the whole weighting process as an ordered recipe of explicit adjustments, estimated in a single call. Steps cover within-cluster selection, subsampling for two-phase designs, nonresponse by weighting classes or response-propensity models (optionally machine-learning, with cross-fitting), calibration to known totals following Deville and Sarndal (1992) <doi:10.2307/2290268>, optionally model-assisted, non-probability samples by pseudo-weighting, mass imputation and doubly robust estimators, and range-restricted trimming. Rotating and pure panels add panel-selection probabilities, attrition, longitudinal weights, gross flows and composite estimation. Variances come from a recipe-aware bootstrap and jackknife that resample or delete primary sampling units and re-apply the entire cascade on each replicate, following Rao and Wu (1988) <doi:10.1080/01621459.1988.10478591>; panel replicates are coordinated across waves, so the sample overlap enters the variance of a net change as covariance, and two-phase variances split into first- and second-phase components (V = V1 + V2). A self-contained HTML report documents each step, and the weights bridge to the 'survey' and 'srvyr' packages. The methods, and the simulation evidence behind the variance estimators, are described in Ferreira (2026) <doi:10.1177/18747655261484262>.

Version:	1.3.0
Depends:	R (≥ 4.1.0)
Imports:	stats, utils, graphics, parallel
Suggests:	MASS, rpart, ranger, testthat (≥ 3.0.0), survey, srvyr, dplyr, tidyr, ggplot2, haven, archive, knitr, rmarkdown, spelling, xgboost, yaml
Published:	2026-09-11
DOI:	10.32614/CRAN.package.weightflow
Author:	Juan Pablo Ferreira [aut, cre, cph], Andrés Gutiérrez [aut]
Maintainer:	Juan Pablo Ferreira <juanpablo.ferreira at fcea.edu.uy>
BugReports:	https://github.com/jpferreira33/weightflow/issues
License:	MIT + file LICENSE
URL:	https://github.com/jpferreira33/weightflow, https://jpferreira33.github.io/weightflow/
NeedsCompilation:	no
Language:	en-US
Citation:	weightflow citation info
Materials:	README, NEWS
In views:	OfficialStatistics
CRAN checks:	weightflow results

Documentation:

Reference manual:

weightflow.html , weightflow.pdf

Vignettes:

Machine learning, cross-fitting and robust calibration (source, R code) Ways to specify calibration totals (source, R code) Calibration: raking, post-stratification and GREG (source, R code) Composite estimation: borrowing strength from the previous wave (source, R code) Coordinated replication: what travels between waves (source, R code) Inspecting and auditing the cascade (source, R code) Model calibration (model-assisted weighting) (source, R code) Non-probability samples (source, R code) Nonresponse: weighting classes, propensities and calibration (source, R code) Pure panels: the longitudinal weight and gross flows (source, R code) Preparing the sample: eligibility and response before weighting (source, R code) Documenting and auditing the weights: the quality report (source, R code) From raw sample to final weights (source, R code) Calibrating to a reference survey (source, R code) Rotating panels: net change, chaining and gross flows (source, R code) Trimming survey weights (source, R code) Two-phase (double) sampling (source, R code) Validation against survey and ReGenesees (source, R code) Variance estimation (source, R code) weightflow in production (GSBPM 5.6) (source, R code) Staged survey weighting: the adjustment logic (source, R code)

Downloads:

Package source:	weightflow_1.3.0.tar.gz
Windows binaries:	r-devel: weightflow_1.2.0.zip, r-release: weightflow_1.2.0.zip, r-oldrel: weightflow_1.3.0.zip
macOS binaries:	r-release (arm64): weightflow_1.2.0.tgz, r-oldrel (arm64): weightflow_1.3.0.tgz, r-release (x86_64): weightflow_1.3.0.tgz, r-oldrel (x86_64): weightflow_1.3.0.tgz
Old sources:	weightflow archive

Linking:

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