https://jpferreira33.r-universe.devRecent package updates in jpferreira33R-universehttps://github.com/jpferreira33.pnghttps://jpferreira33.r-universe.devThu, 02 Jul 2026 17:13:40 GMTjuanpablo.ferreira@fcea.edu.uy (Juan Pablo Ferreira)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.https://github.com/r-universe/jpferreira33/actions/runs/28609981471Thu, 02 Jul 2026 17:13:40 GMTweightflow0.1.0successhttps://jpferreira33.r-universe.devhttps://github.com/jpferreira33/weightflowcalibration.Rmdcalibration.htmlCalibration: raking, post-stratification and GREG2026-06-26 23:03:442026-06-26 23:03:44advanced-methods.Rmdadvanced-methods.htmlMachine learning, cross-fitting and robust calibration2026-06-25 02:57:502026-06-25 02:57:50model-calibration.Rmdmodel-calibration.htmlModel calibration (model-assisted weighting)2026-06-20 14:10:192026-06-26 03:19:50nonresponse-propensities.Rmdnonresponse-propensities.htmlNonresponse: weighting classes and propensities2026-06-20 14:10:192026-06-26 12:37:07weightflow.Rmdweightflow.htmlStaged survey weighting: the adjustment logic2026-06-20 14:10:192026-06-25 11:56:26validation-against-survey.Rmdvalidation-against-survey.htmlValidation against the survey package2026-06-20 14:10:192026-06-20 14:10:19variance-estimation.Rmdvariance-estimation.htmlVariance estimation2026-06-20 03:32:322026-06-26 03:38:40