Overview | Setup | Inspect data | Define population targets | Rake | Inspect results | Design effect | Per-variable diagnostics | Tidy output | Augmenting the data | Advanced options | Weight bounding | Variable selection | Checking discrepancies directly
Package: ipf 0.0.1
Christopher T. Kenny
ipf: Iterative Proportional Fitting
Fast raking for survey weighting. The computational core is written in Rust for speed. Supports multiple raking variables, automatic variable selection, weight bounding, and comprehensive diagnostics.
Authors:
ipf_0.0.1.tar.gz
ipf_0.0.1.zip(r-4.7)ipf_0.0.1.zip(r-4.6)ipf_0.0.1.zip(r-4.5)
ipf_0.0.1.tgz(r-4.6-x86_64)ipf_0.0.1.tgz(r-4.6-arm64)ipf_0.0.1.tgz(r-4.5-x86_64)ipf_0.0.1.tgz(r-4.5-arm64)
ipf_0.0.1.tar.gz(r-4.7-arm64)ipf_0.0.1.tar.gz(r-4.7-x86_64)ipf_0.0.1.tar.gz(r-4.6-arm64)ipf_0.0.1.tar.gz(r-4.6-x86_64)
ipf_0.0.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ipf/json (API)
| # Install 'ipf' in R: |
| install.packages('ipf', repos = c('https://christopherkenny.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/christopherkenny/ipf/issues
Pkgdown/docs site:https://christophertkenny.com
Datasets:
- anes24 - ANES 2024 Time Series Study
Last updated from:284f779430. Checks:12 OK, 1 FAIL. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 203 | ||
| linux-devel-x86_64 | OK | 157 | ||
| source / vignettes | OK | 204 | ||
| linux-release-arm64 | OK | 153 | ||
| linux-release-x86_64 | OK | 152 | ||
| macos-release-arm64 | OK | 116 | ||
| macos-release-x86_64 | OK | 421 | ||
| macos-oldrel-arm64 | OK | 154 | ||
| macos-oldrel-x86_64 | OK | 246 | ||
| windows-devel | OK | 186 | ||
| windows-release | OK | 185 | ||
| windows-oldrel | OK | 133 | ||
| wasm-release | FAIL | 161 |
Exports:augmentdesign_effectfind_discrepant_varsglanceraketidyweight_assess
Dependencies:cligenericsgluelifecyclemagrittrpillarpkgconfigrlangtibbleutf8vctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| ANES 2024 Time Series Study (subset) | anes24 |
| Augment data with raked weights | augment.ipf_rake |
| Compute design effect and effective sample size | design_effect |
| Find discrepant variables and their aggregate discrepancy scores | find_discrepant_vars |
| Glance at an ipf_rake object | glance.ipf_rake |
| Print an ipf_rake object | print.ipf_rake |
| Iterative proportional fitting (raking) | rake |
| Summarize an ipf_rake object | summary.ipf_rake |
| Tidy an ipf_rake object | tidy.ipf_rake |
| Assess weight quality with diagnostic tables | weight_assess |