The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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 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

Documentation:

Reference manual: predieval.pdf

Downloads:

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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=predieval to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.