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
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
NeedsCompilation: no
Packaged: 2026-10-16 01:52:17 UTC; jp
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>
Repository: CRAN
Date/Publication: 2026-10-16 02:30:02 UTC
