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 |
| Author: | Orestis Efthimiou |
| Maintainer: | Orestis Efthimiou <oremiou at gmail.com> |
| 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 |
| Reference manual: | predieval.pdf |
| Package source: | predieval_0.1.1.tar.gz |
| Windows binaries: | r-devel: predieval_0.1.1.zip, r-release: predieval_0.1.1.zip, r-oldrel: predieval_0.1.1.zip |
| macOS binaries: | r-release (arm64): predieval_0.1.1.tgz, r-oldrel (arm64): predieval_0.1.1.tgz, r-release (x86_64): predieval_0.1.1.tgz, r-oldrel (x86_64): predieval_0.1.1.tgz |
| Old sources: | predieval archive |
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