PredictABEL: Assessment of Risk Prediction Models

We included functions to assess the performance of risk models. The package contains functions for the various measures that are used in empirical studies, including univariate and multivariate odds ratios (OR) of the predictors, the c-statistic (or area under the receiver operating characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test, reclassification table, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Also included are functions to create plots, such as risk distributions, ROC curves, calibration plot, discrimination box plot and predictiveness curves. In addition to functions to assess the performance of risk models, the package includes functions to obtain weighted and unweighted risk scores as well as predicted risks using logistic regression analysis. These logistic regression functions are specifically written for models that include genetic variables, but they can also be applied to models that are based on non-genetic risk factors only. Finally, the package includes function to construct a simulated dataset with genotypes, genetic risks, and disease status for a hypothetical population, which is used for the evaluation of genetic risk models.

Version: 1.2-4
Depends: R (≥ 2.12.0)
Imports: Hmisc, ROCR, PBSmodelling, lazyeval, methods
Published: 2020-03-09
DOI: 10.32614/CRAN.package.PredictABEL
Author: Suman Kundu, Yurii S. Aulchenko, A. Cecile J.W. Janssens
Maintainer: Suman Kundu <suman_math at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: PredictABEL results


Reference manual: PredictABEL.pdf


Package source: PredictABEL_1.2-4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): PredictABEL_1.2-4.tgz, r-oldrel (arm64): PredictABEL_1.2-4.tgz, r-release (x86_64): PredictABEL_1.2-4.tgz, r-oldrel (x86_64): PredictABEL_1.2-4.tgz
Old sources: PredictABEL archive


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