Title: Regression Optimized: Numerical Approach for Multivariate
Author: Jean-Michel Nguyen [aut, cre], Daniel Antonioli [aut]
Description: Trees Classification and Regression using multivariate nodes calculated by an exhaustive numerical approach. We propose a new concept of decision tree, including multivariate knots and non hierarchical pathway. This package's model uses a multivariate nodes tree that calculates directly a risk score for each observation for the state Y observed. Nguyen JM, Gaultier A, Antonioli D (2015) <doi:10.1016/j.respe.2018.03.088> Castillo JM, Knol AC, Nguyen JM, Khammari A, Saint Jean M, Dreno B (2016) <doi:10.1684/ejd.2016.2826> Vildy S, Nguyen JM, Gaultier A, Khammari A, Dreno B (2017) <doi:10.1684/ejd.2016.2955> Nguyen JM, Gaultier A, Antonioli D (2018) <doi:10.1016/j.respe.2018.03.088>.

Title: ROSE: Random Over-Sampling Examples
Authors@R: c(person("Nicola", "Lunardon", role = c("aut", "cre"), email
"Menardi", role = c("aut", "cre"), email =
"menardi@stat.unipd.it"), person("Nicola", "Torelli", role =
Author: Nicola Lunardon <nicola.lunardon@econ.units.it>, Giovanna
Maintainer: Nicola Lunardon <nicola.lunardon@econ.units.it>, Giovanna
Description: The package provides functions to deal with binary
classification problems in the presence of imbalanced classes.
Synthetic balanced samples are generated according to ROSE
(Menardi and Torelli, 2012).  Functions that implement more
traditional remedies to the class imbalance are also provided,
as well as different metrics to evaluate a learner accuracy.
These are estimated by holdout, bootstrap or cross-validation

Author: Nicola Lunardon, Giovanna Menardi, Nicola Torelli
Description: The package provides functions to deal with binary classification
problems in the presence of imbalanced classes. Synthetic balanced samples are
generated according to ROSE (Menardi and Torelli, 2013).
Functions that implement more traditional remedies to the class imbalance
are also provided, as well as different metrics to evaluate a learner accuracy.
These are estimated by holdout, bootstrap or cross-validation methods.