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.

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 <oremiou at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: predieval results


Reference manual: predieval.pdf


Package source: predieval_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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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