Package: predieval 0.1.2
Orestis Efthimiou
predieval: Assessing Performance of Prediction Models for Predicting Patient-Level Treatment Benefit
Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.
Authors:
predieval_0.1.2.tar.gz
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predieval_0.1.2.tgz(r-4.5-any)predieval_0.1.2.tgz(r-4.4-any)predieval_0.1.2.tgz(r-4.3-any)
predieval_0.1.2.tar.gz(r-4.5-noble)predieval_0.1.2.tar.gz(r-4.4-noble)
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predieval.pdf |predieval.html✨
predieval/json (API)
NEWS
| # Install 'predieval' in R: |
| install.packages('predieval', repos = c('https://esm-ispm-unibe-ch.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/esm-ispm-unibe-ch/predieval/issues
Last updated 2 years agofrom:225b2573be. Checks:8 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 10 2025 |
| R-4.5-win | OK | Feb 10 2025 |
| R-4.5-mac | OK | Feb 10 2025 |
| R-4.5-linux | OK | Feb 10 2025 |
| R-4.4-win | OK | Feb 10 2025 |
| R-4.4-mac | OK | Feb 10 2025 |
| R-4.3-win | OK | Feb 10 2025 |
| R-4.3-mac | OK | Feb 10 2025 |
Exports:bencalibrexpitlogitpredievalsimbinarysimcont
Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatchingMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml
