Suggests: doParallel, dplyr, tidyr, knitr, rmarkdown

time-series data, such as EEG data, using permutation testing (Maris & Oostenveld, 2007)
types of heatmap for reporting. Mixed-effects models are supported using an implementation of
Suggests: buildmer (>= 1.7.1), car, doParallel, dplyr, ggplot2, lmPerm,
lme4, permuco, pbkrtest, tidyr, knitr, rmarkdown, viridis

lme4, permuco, knitr, rmarkdown, viridis

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

Title: Resampling-based prediction error estimation for regression
Depends: R (>= 2.14.1), ggplot2, parallel, robustbase
prediction error estimation with minimal programming effort and
assist users with model selection in regression problems.
Authors@R: person("Andreas", "Alfons", email =
"andreas.alfons@kuleuven.be", role = c("aut", "cre"))
Collate: 'accessors.R' 'aggregate.R' 'cost.R' 'fortify.R' 'perryFit.R'
'print.R' 'reperry.R' 'selectBest.R' 'subset.R' 'summary.R'

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

Authors@R: person("Andreas", "Alfons", email = "alfons@ese.eur.nl",

Title: Resampling-Based Prediction Error Estimation for Regression
Description: Tools that allow developers to write functions for prediction
error estimation with minimal programming effort and assist users with

Title: Examples for Integrating Prediction Error Estimation into
Depends: R (>= 2.14.1), perry (>= 0.3.0), robustbase
Description: Examples for integrating package 'perry' for prediction error estimation into regression models.

Title: Estimating Personalized Diagnostics Rules
Description: Recommend the optimal biomarker (or test) for disease screening or diagnosis based on patients' individual characteristics.

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

Title: Estimation and Validation Methods for Subgroup Identification
Description: 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