Vignettes and other documentation

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Vignettes from package 'weightflow'

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