Depends: R (>= 3.5.0), survival, pROC, risksetROC, mgcv, snowfall

Title: Selecting Combinations of Predictors by Leveraging Multiple AUCs
Description: Uses multiple AUCs to select a combination of predictors when the outcome has multiple (ordered) levels and the focus is discriminating one particular level from the others. This method is most naturally applied to settings where the outcome has three levels. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper423/>.)

Author: Matieyendou LAMBONI <clamboni@yahoo.fr>, Herve MONOD
Description: An R library for performing sensitivity analysis on a

Description: An R package to perform sensitivity analysis on a model

Description: An R package to perform sensitivity analysis on a

Description: Functions to perform sensitivity analysis on a model with multivariate output.

Author: Caroline Bidot <caroline.bidot@jouy.inra.fr>, Matieyendou Lamboni <clamboni@yahoo.fr>, Hervé Monod <herve.monod@jouy.inra.fr>
Imports: stats, graphics, utils, grDevices, sensitivity, knitr

Author: Caroline Bidot <caroline.bidot@inra.fr>, Matieyendou Lamboni <matieyendou.lamboni@gmail.com>, Hervé Monod <herve.monod@inra.fr>

Title: Estimation of Accuracy in Multisite Machine-Learning Models
Description: The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the 'training set' and when applying the model in the 'test set'. This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly as described in (Solanes et al, Psychiatry Research: Neuroimaging 2021, in Press). It currently supports the estimation of sensitivity, specificity, balanced accuracy, the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, gaussian, and survival (time-to-event) outcomes.
Imports: AROC, coxme, lme4, lmerTest, logistf, metafor, pROC, survival

Description: The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the 'training set' and applying the model in the 'test set'. This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly, as described in (Solanes et al., Psychiatry Research: Neuroimaging 2021, 314:111313). It currently supports the estimation of sensitivity, specificity, balanced accuracy (for binary or multinomial variables), the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, multinomial, gaussian, and survival (time-to-event) outcomes.