modelROC: Model Based ROC Analysis

The ROC curve method is one of the most important and commonly used methods for model accuracy assessment, which is one of the most important elements of model evaluation. The 'modelROC' package is a model-based ROC assessment tool, which directly works for ROC analysis of regression results for logistic regression of binary variables, including the glm() and lrm() commands, and COX regression for survival analysis, including the cph() and coxph() commands. The most important feature of 'modelROC' is that both the model and the independent variables can be analysed simultaneously, and for survival analysis multiple time points and area under the curve analysis are supported. Still, flexible visualisation is possible with the 'ggplot2' package. Reference are Kelly H. Zou (1998) <doi:10.1002/(sici)1097-0258(19971015)16:19%3C2143::aid-sim655%3E3.0.co;2-3> and P J Heagerty (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.

Version: 1.0
Depends: ggplot2
Imports: do, tmcn, ROCit, survivalROC
Suggests: ggDCA, rms
Published: 2021-06-25
Author: Jing Zhang [aut, cre], Zhi Jin [aut]
Maintainer: Jing Zhang <zj391120 at 163.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: modelROC results

Downloads:

Reference manual: modelROC.pdf
Package source: modelROC_1.0.tar.gz
Windows binaries: r-devel: modelROC_1.0.zip, r-release: modelROC_1.0.zip, r-oldrel: modelROC_1.0.zip
macOS binaries: r-release (arm64): modelROC_1.0.tgz, r-release (x86_64): modelROC_1.0.tgz, r-oldrel: modelROC_1.0.tgz

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