Why the adjustments matter for variance | Method 1: a PSU bootstrap that re-applies the recipe | Estimates with bootstrap standard errors | Method 2: hand the weights to the survey package | Replicate weights for a tidyverse workflow | Which one to use

What calibration does | Why calibration reduces variance: the regression link | Post-stratification | Raking | Linear calibration (GREG) | Bounded calibration | Equal weights within a cluster | Which to use | References

Weighting classes | Response propensities | Logistic regression | Trees, forests and boosting | Flexibility, overfitting, and cross-fitting | Person or household level | Which to use

Why the adjustments matter for variance | Method 1: a PSU bootstrap that re-applies the recipe | Estimates with bootstrap standard errors | Method 2: hand the weights to the survey package | Replicate weights for a tidyverse workflow | Which one to use

The idea | In weightflow, with a random forest | Validation: the constraints are met exactly | Does it help the estimate? | What you need: unit-level auxiliaries | The hybrid: model term and consistency constraints | Relationship to the literature

The starting point: design weights | The cascade | Unknown eligibility | Ineligible units | Within-household selection | Nonresponse | Calibration | Trimming, rounding, rescaling | Reading the cascade: the design effect | Why the order matters

Flexible propensities | The overfitting problem | Cross-fitting | Ridge (penalized) calibration | Potter (MSE-optimal) trimming | Putting it together | References

Post-stratification | Raking | Linear (GREG) calibration | Same estimates | What weightflow adds