c(
person("Sarah", "McGough", email = "sfm341@mail.harvard.edu", role = c("aut", "cre")),
person("Nicolas", "Menzies", email = "nmenzies@hsph.harvard.edu", role = "aut"),
person("Marc", "Lipsitch", email = "mlipsitc@hsph.harvard.edu", role = "aut"),
person("Michael", "Johansson", email = "mjohansson@cdc.gov", role = "aut")
)
A Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. 'NobBS' learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings, as described in McGough et al. (2019) <doi:10.1101/663823>.
JAGS (http://mcmc-jags.sourceforge.net/) for
analysis of Bayesian hierarchical models
Sarah McGough [aut, cre],
Nicolas Menzies [aut],
Marc Lipsitch [aut],
Michael Johansson [aut]

Label Noise Filters for Data Preprocessing in Classification
c(
person("Pablo", "Morales", email = "pablomoralvarez@gmail.com", role = c("aut")),
person("Julian", "Luengo", email = "julianlm@decsai.ugr.es", role = c("aut","cre")),
person("Luis P.F.", "Garcia", role = c("aut")),
person("Ana C.", "Lorena", role = c("aut")),
person("Andre C.P.L.F.", "de Carvalho", role = c("aut")),
person("Francisco", "Herrera", role = c("aut")))
An extensive implementation of state-of-the-art and classical
algorithms to preprocess label noise in classification problems.
RWeka, kknn, nnet, caret, e1071, rpart, randomForest, MASS,
rJava, stats, utils
Pablo Morales [aut],
Julian Luengo [aut, cre],
Luis P.F. Garcia [aut],
Ana C. Lorena [aut],
Andre C.P.L.F. de Carvalho [aut],
Francisco Herrera [aut]

Biplot representations of categorical data
Julio Cesar Hernandez Sanchez, Jose Luis Vicente-Villardon
Julio Cesar Hernandez Sanchez <juliocesar_avila@usal.es>
Analysis of a matrix of polytomous items using Nominal Logistic Biplots (NLB)
according to Hernandez-Sanchez and Vicente-Villardon (2013).
The NLB procedure extends the binary logistic biplot to nominal (polytomous) data.
The  individuals are represented as points on a plane and the  variables are represented
as convex prediction regions rather than vectors as in a classical or binary biplot.
Using the methods from Computational Geometry, the set of prediction regions is converted to a set of points
in such a way that the prediction for each individual is established by its closest
"category point". Then interpretation is based on distances rather than on projections.
In this package we implement the geometry of such a representation and construct computational algorithms
for the estimation of parameters and the calculation of prediction regions.