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
predieval_0.1.2.zip(r-4.5)predieval_0.1.2.zip(r-4.4)predieval_0.1.2.zip(r-4.3)
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)
predieval_0.1.2.tgz(r-4.4-emscripten)predieval_0.1.2.tgz(r-4.3-emscripten)
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:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 12 2024 |
| R-4.5-win | OK | Nov 12 2024 |
| R-4.5-linux | OK | Nov 12 2024 |
| R-4.4-win | OK | Nov 12 2024 |
| R-4.4-mac | OK | Nov 12 2024 |
| R-4.3-win | OK | Nov 12 2024 |
| R-4.3-mac | OK | Nov 12 2024 |
Exports:bencalibrexpitlogitpredievalsimbinarysimcont
Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatchingMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml
