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predieval  

Assessing Performance of Prediction Models for Predicting Patient-Level Treatment Benefit
View on CRAN: Click here


Download and install predieval package within the R console
Install from CRAN:
install.packages("predieval")

Install from Github:
library("remotes")
install_github("cran/predieval")

Install by package version:
library("remotes")
install_version("predieval", "0.1.1")



Attach the package and use:
library("predieval")
Maintained by
Orestis Efthimiou
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-03-28
Latest Update: 2022-04-19
Description:
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.
How to cite:
Orestis Efthimiou (2022). predieval: Assessing Performance of Prediction Models for Predicting Patient-Level Treatment Benefit. R package version 0.1.1, https://cran.r-project.org/web/packages/predieval. Accessed 10 Mar. 2026.
Previous versions and publish date:
0.1.0 (2022-03-28 10:10)
Other packages that cited predieval R package
View predieval citation profile
Other R packages that predieval depends, imports, suggests or enhances
Complete documentation for predieval
Functions, R codes and Examples using the predieval R package
Some associated functions: bencalibr . datbinary . datcont . expit . logit . predieval . simbinary . simcont . 
Some associated R codes: bencalibr.R . datbinary.R . datcont.R . predieval.R . simbinary.R . simcont.R .  Full predieval package functions and examples
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