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
predieval_0.1.2.zip(r-4.7-any)predieval_0.1.2.zip(r-4.6-any)predieval_0.1.2.zip(r-4.5-any)
predieval_0.1.2.tgz(r-4.6-any)predieval_0.1.2.tgz(r-4.5-any)
predieval_0.1.2.tar.gz(r-4.7-any)predieval_0.1.2.tar.gz(r-4.6-any)
predieval_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
predieval/json (API)

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

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

On CRAN:

Conda:

2.00 score 1 stars 1 scripts 267 downloads 6 exports 57 dependencies

Last updated from:aca97e6e8f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK141
source / vignettesOK237
linux-releaseOK220
macos-releaseOK210
macos-oldrelOK223
windows-develOK91
windows-releaseOK90
windows-oldrelOK88
wasm-releaseOK108

Exports:bencalibrexpitlogitpredievalsimbinarysimcont

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacecpp11data.tabledigestevaluatefarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglifecyclemagrittrMASSMatchingmemoisemimennetR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapiS7sassscalesstringistringrtinytexvctrsviridisLitewithrxfunyaml