Package: predieval 0.1.2

Orestis Efthimiou

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.

Authors:Orestis Efthimiou

predieval_0.1.2.tar.gz
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predieval.pdf |predieval.html
predieval/json (API)
NEWS

# Install 'predieval' in R:
install.packages('predieval', repos = c('https://esm-ispm-unibe-ch.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/esm-ispm-unibe-ch/predieval/issues

On CRAN:

2.00 score 1 scripts 99 downloads 6 exports 66 dependencies

Last updated 2 years agofrom:225b2573be. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 13 2024
R-4.5-winOKOct 13 2024
R-4.5-linuxOKOct 13 2024
R-4.4-winOKOct 13 2024
R-4.4-macOKOct 13 2024
R-4.3-winOKOct 13 2024
R-4.3-macOKOct 13 2024

Exports:bencalibrexpitlogitpredievalsimbinarysimcont

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatchingMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml