Package: weightflow
Title: Weighting Probability and Non-Probability Survey Samples with
        Recipe-Aware Replicate Variances
Version: 1.3.1
Authors@R: c(
    person("Juan Pablo", "Ferreira", email = "juanpablo.ferreira@fcea.edu.uy",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-1884-8187")),
    person("Andrés", "Gutiérrez", role = "aut",
           comment = c(ORCID = "0009-0007-2918-1932")))
Description: Builds survey analysis weights by declaring the whole
        weighting process as an ordered recipe of explicit adjustments,
        estimated in a single call: unknown eligibility, within-cluster
        selection, two-phase subsampling, nonresponse by weighting
        classes or response-propensity models, calibration and
        trimming. Calibration to known totals runs on linear, raking or
        logit distances, bounded, ridge-penalized, model-assisted or
        integrative with one weight per cluster; a calibrated trim
        bounds the weights while keeping the control totals exact.
        Non-probability samples are weighted against a probability
        reference sample by pseudo-weighting, mass imputation and
        doubly robust estimators that calibrate a single weight on the
        fitted values of several working models and on the population
        totals of the auxiliaries at once, so one multipurpose weight
        serves many variables. Variances come from a recipe-aware
        bootstrap and jackknife that re-apply the entire cascade on
        each replicate, so every adjustment is inside the variance
        instead of being treated as fixed; across panel waves the
        replicates are coordinated by carrying forward the selection
        multiplicities of the sampling units, so the sample overlap
        enters the variance of a net change as covariance. Rotating and
        pure panels add attrition, longitudinal weights, gross flows
        and composite estimation, a self-contained HTML report
        documents each step, and the weights bridge to the 'survey' and
        'srvyr' packages, which are optional: the package itself runs
        on base R. The methods, and the simulation evidence behind the
        variance estimators, are described in Ferreira (2026)
        <doi:10.1177/18747655261484262>.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
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
Roxygen: list(markdown = TRUE, roclets = c("rd"))
Config/roxygen2/version: 8.0.0
URL: https://github.com/jpferreira33/weightflow,
        https://jpferreira33.github.io/weightflow/
BugReports: https://github.com/jpferreira33/weightflow/issues
Config/testthat/edition: 3
LazyData: true
VignetteBuilder: knitr
Repository: https://jpferreira33.r-universe.dev
Date/Publication: 2026-10-16 01:50:47 UTC
RemoteUrl: https://github.com/jpferreira33/weightflow
RemoteRef: HEAD
RemoteSha: b895824695e80544d2a044f3c56cddb804827e1f
NeedsCompilation: no
Packaged: 2026-10-16 07:29:32 UTC; root
Author: Juan Pablo Ferreira [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-1884-8187>),
  Andrés Gutiérrez [aut] (ORCID: <https://orcid.org/0009-0007-2918-1932>)
Maintainer: Juan Pablo Ferreira <juanpablo.ferreira@fcea.edu.uy>