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::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::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::trimming		Trimming survey weights	HTML	source	R code
weightflow::two-phase-sampling		Two-phase (double) sampling	HTML	source	R code
weightflow::validation-against-survey		Validation against the survey package	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