c(person("Nicola", "Lunardon", role = c("aut", "cre"), email
= "nicola.lunardon@econ.units.it"), person("Giovanna",
"Menardi", role = c("aut", "cre"), email =
"menardi@stat.unipd.it"), person("Nicola", "Torelli", role =
"aut", email = "nicola.torelli@econ.units.it"))
Nicola Lunardon <nicola.lunardon@econ.units.it>, Giovanna
Menardi <menardi@stat.unipd.it>, Nicola Torelli
<nicola.torelli@econ.units.it>
Nicola Lunardon <nicola.lunardon@econ.units.it>, Giovanna
Menardi <menardi@stat.unipd.it>
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
methods.

Nicola Lunardon, Giovanna Menardi, Nicola Torelli
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.

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.

R-Interface to the observations Data Model from QUASHI
RMySQL (>= 0.9-3), RSQLite, SSOAP, XMLSchema, testthat, XML,
maptools, sp, RColorBrewer, RPostgreSQL (>= 0.4), geonames
This package provides classes and functions for handling observational (time series) data