Imports: dlstats, rvcheck, desc, usethis (>= 2.0.0)

person("Alexander", "Rossell Hayes", email = "alexander@rossellhayes.com", role = "ctb", comment = c(ORCID = "0000-0001-9412-0457", github = "rossellhayes")),
person("Waldir", "Leoncio",          email = "w.l.netto@medisin.uio.no",   role = "ctb")

Title: Make Waffle Plots with Base Graphics
Description: Waffle plots are rectangular pie charts that represent a quantity or abundances using
colored squares or other symbol. This makes them better at transmitting information as the
discrete number of squares is easier to read than the circular area of pie charts.
While the original waffle charts were rectangular with 10 rows and columns, with a single square
representing 1%, they are nowadays popular in various infographics to visualize any
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, png

Title: Ensemble bagging and boosting classifiers
Description: bagRboostR is a set of ensemble classifiers for
multinomial classification. The bagging function is the
implementation of Breiman's ensemble as described by Opitz &
Maclin (1999). The boosting function is the implementation of
Stagewise Additive Modeling using a Multi-class Exponential
loss function (SAMME) created by Zhu et al (2006). Both bagging
and SAMME implementations use randomForest as the weak
classifier and expect a character outcome variable.  Each
ensemble classifier returns a character vector of predictions
Packaged: 2013-03-23 22:02:55 UTC; shannonmrush

Description: bagRboostR is a set of ensemble classifiers for multinomial
classification. The bagging function is the implementation of Breiman's
ensemble as described by Opitz & Maclin (1999). The boosting function is
the implementation of Stagewise Additive Modeling using a Multi-class
Exponential loss function (SAMME) created by Zhu et al (2006). Both bagging
and SAMME implementations use randomForest as the weak classifier and
expect a character outcome variable.  Each ensemble classifier returns a
character vector of predictions for the test set.
Packaged: 2014-03-05 16:29:22 UTC; shannonmrush