Matieyendou LAMBONI <clamboni@yahoo.fr>, Herve MONOD
<herve.monod@jouy.inra.fr>
An R library for performing sensitivity analysis on a
model with multivariate output

Matieyendou LAMBONI <clamboni@yahoo.fr>, Herve MONOD
<herve.monod@jouy.inra.fr>, Caroline BIDOT
<caroline.bidot@jouy.inra.fr>
An R package to perform sensitivity analysis on a model
with multivariate output

An R package to perform sensitivity analysis on a
model with multivariate output

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

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

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

Estimation of Accuracy in Multisite Machine-Learning Models
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.
AROC, coxme, lme4, lmerTest, logistf, metafor, pROC, survival

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.