What is regrake? | Setup | Basic raking with exact constraints | Adding complexity: mixed constraints | Checking balance | When is regrake worth the complexity? | Feature reference
Package: regrake 1.0.0
regrake: Regularized Survey Raking
Calibrates survey weights to known population targets using regularized raking. Constraints are specified with a formula interface (for example, rr_exact(), rr_l2(), rr_range(), rr_mean(), rr_var(), and rr_quantile()). Supports common target formats including autumn-style proportions tables, raw or weighted population microdata, named-list targets (as in 'anesrake'), and 'survey' package design objects. Optimization follows Barratt et al. (2021) <https://web.stanford.edu/~boyd/papers/pdf/optimal_representative_sampling.pdf> and returns calibrated weights with balance and convergence diagnostics.
Authors:
regrake_1.0.0.tar.gz
regrake_1.0.0.zip(r-4.7)regrake_1.0.0.zip(r-4.6)regrake_1.0.0.zip(r-4.5)
regrake_1.0.0.tgz(r-4.6-any)regrake_1.0.0.tgz(r-4.5-any)
regrake_1.0.0.tar.gz(r-4.7-any)regrake_1.0.0.tar.gz(r-4.6-any)
regrake_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
regrake/json (API)
| # Install 'regrake' in R: |
| install.packages('regrake', repos = c('https://andytimm.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andytimm/regrake/issues
Last updated from:ba8be4d354. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 127 | ||
| source / vignettes | OK | 192 | ||
| linux-release-x86_64 | OK | 169 | ||
| macos-release-arm64 | OK | 84 | ||
| macos-oldrel-arm64 | OK | 86 | ||
| windows-devel | OK | 95 | ||
| windows-release | OK | 105 | ||
| windows-oldrel | OK | 111 | ||
| wasm-release | OK | 111 |
Exports:regrakerr_betweenrr_exactrr_klrr_l2rr_meanrr_quantilerr_rangerr_var
Dependencies:clidigestgluelamWlatticelifecyclemagrittrMatrixpillarpkgconfigRcppRcppParallelrlangtibbleutf8vctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Print method for raking_formula objects | print.raking_formula |
| Print method for regrake objects | print.regrake |
| Proximal operator for boolean regularizer | prox_boolean_reg |
| Proximal operator for equality constraints | prox_equality |
| Proximal operator for equality regularizer | prox_equality_reg |
| Proximal operator for inequality constraints | prox_inequality |
| Proximal operator for KL divergence loss | prox_kl |
| Proximal operator for KL regularizer | prox_kl_reg |
| Proximal operator for least squares loss | prox_least_squares |
| Proximal operator for sum squares regularizer | prox_sum_squares_reg |
| Optimal representative sample weighting | regrake |
| Raking Constraint Functions | rr_between rr_constraints rr_exact rr_kl rr_l2 rr_mean rr_quantile rr_range rr_var |
| Summary method for regrake objects | summary.regrake |
