buildmer (>= 1.7.1), car, doParallel, dplyr, ggplot2, lmPerm,
lme4, permuco, knitr, rmarkdown, viridis

buildmer (>= 2.3), car, doParallel, ggplot2, glmmTMB, knitr,
lme4, lmPerm, permuco, rmarkdown, viridis

Resampling-based prediction error estimation for regression
models
R (>= 2.14.1), ggplot2, parallel, robustbase
Tools that allow developers to write functions for
prediction error estimation with minimal programming effort and
assist users with model selection in regression problems.
person("Andreas", "Alfons", email =
"andreas.alfons@kuleuven.be", role = c("aut", "cre"))
'accessors.R' 'aggregate.R' 'cost.R' 'fortify.R' 'perryFit.R'
'perryPlot.R' 'perry.R' 'perryReshape.R' 'perrySelect.R'
'perrySplits.R' 'perryTools.R' 'perryTuning.R' 'predict.R'
'print.R' 'reperry.R' 'selectBest.R' 'subset.R' 'summary.R'
'utils.R'

R (>= 2.14.1), ggplot2 (>= 0.9.2), parallel, robustbase

person("Andreas", "Alfons", email = "alfons@ese.eur.nl",
role = c("aut", "cre"))

Resampling-Based Prediction Error Estimation for Regression
Models
Tools that allow developers to write functions for prediction
error estimation with minimal programming effort and assist users with
model selection in regression problems.
person("Andreas", "Alfons",
email = "alfons@ese.eur.nl",
role = c("aut", "cre"))

Examples for Integrating Prediction Error Estimation into
Regression Models
Examples for integrating package 'perry' for prediction error estimation into regression models.

Recommend the optimal biomarker (or test) for disease screening or diagnosis based on patients' individual characteristics.

Sadegh Rasoulinejad (R package and database version of the
dictionary) Manouchehr Aryanpour (for the original dictionary)
Translate words from English to Persian (Over 67,000
words)

Estimation and Validation Methods for Subgroup Identification
and Personalized Medicine
Provides functions for fitting and validation of subgroup
identification and personalized medicine models under the general subgroup
identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>.
This package is intended for use for both randomized controlled trials and
observational studies.