Authors@R: c(person("Vincent Dorie",    "Developer", role = c("aut", "cre"), email = "vdorie@gmail.com"),
person("Hugh Chipman",     "Developer", role = "aut",           email = "hugh.chipman@gmail.com"),
person("Robert McCulloch", "Developer", role = "aut",           email = "robert.mcculloch1@gmail.com"))
Author: Vincent Dorie Developer [aut, cre],
Maintainer: Vincent Dorie Developer <vdorie@gmail.com>

Description: Fits Bayesian additive regression trees (BART) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/MH sampler. Also serves as a drop-in replacement for package 'BayesTree'.

Authors@R: c(person("Vincent", "Dorie",     role = c("aut", "cre"), email = "vdorie@gmail.com"),
person("Hugh",    "Chipman",   role = "aut",           email = "hugh.chipman@gmail.com"),
person("Robert",  "McCulloch", role = "aut",           email = "robert.mcculloch1@gmail.com"))

Description: Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/MH sampler. Also serves as a drop-in replacement for package 'BayesTree'.

Imports: stats, methods, graphics, parallel

Title: Distance-Based Common Spatial Patterns
Authors@R: c(person("Itziar","Irigoien", email="itziar.irigoien@ehu.eus", role="aut"), person("Concepción","Arenas", email="carenas@ub.edu", role="aut"), person("Itsaso", "Rodríguez-Moreno",email="itsaso.rodriguez@ehu.eus",role=c("cre","aut")))
Description: A way to apply Distance-Based Common Spatial Patterns (DB-CSP) techniques in different fields, both classical Common Spatial Patterns (CSP) as well as DB-CSP. The method is composed of two phases: applying the DB-CSP algorithm and performing a classification. The main idea behind the CSP is to use a linear transform to project data into low-dimensional subspace with a projection matrix, in such a way that each row consists of weights for signals. This transformation maximizes the variance of two-class signal matrices.The dbcsp object is created to compute the projection vectors. For exploratory and descriptive purpose, plot and boxplot functions can be used. Functions train, predict and selectQ are implemented for the classification step.
Imports: plyr, parallelDist, Matrix, geigen, caret, MASS, stats,