predieval: Assessing Performance of Prediction Models for Predicting Patient-Level Treatment Benefit (original) (raw)
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.
| Version: | 0.1.1 |
|---|---|
| Depends: | R (≥ 4.1) |
| Imports: | stats, Hmisc (≥ 4.6-0), ggplot2 (≥ 3.3.5), MASS (≥ 7.3), Matching (≥ 4.10-2) |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2022-04-19 |
| DOI: | 10.32614/CRAN.package.predieval |
| Author: | Orestis Efthimiou |
| Maintainer: | Orestis Efthimiou |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/esm-ispm-unibe-ch/predieval |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | predieval results |
Documentation:
Downloads:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=predievalto link to this page.