Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems.

Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.

c(person("Zhu", "Wang", role = c("aut", "cre"),
email = "wangz1@uthscsa.edu"),
person("Torsten", "Hothorn", role = "ctb"))
rpart, methods, foreach, doParallel, gbm

The package contains HingeBoost for binary and multi-class
classification, with unequal misclassification costs for binary
case. Functional gradient descent algorithm to optimize the
hinge loss. The algorithm can fit linear and nonlinear
classifiers.

c(person("Zhu", "Wang", role = c("aut", "cre"),
email = "wangz1@uthscsa.edu",
comment = c(ORCID = "0000-0002-0773-0052")),
person("Torsten", "Hothorn", role = "ctb"))
Zhu Wang [aut, cre] (<https://orcid.org/0000-0002-0773-0052>),
Torsten Hothorn [ctb]

The package contains HingeBoost for binary and multi-class classification, with unequal misclassification costs for binary case. Functional gradient descent algorithm to optimize the hinge loss. The algorithm can fit linear and nonlinear classifiers.

c(
person(given = "Brenda",
family = "Betancourt",
role = c("aut"),
email = "bbetancourt@ufl.edu"),
person(given = "Andee",
family = "Kaplan",
role = c("aut"),
email = "andee.kaplan@colostate.edu"),
person(given = "Ian",
family = "Taylor",
role = c("aut", "cre"),
email = "ian.taylor@colostate.edu")
)
Perform record linkage on streaming files using recursive Bayesian updating.
BRL, doParallel, extraDistr, foreach, parallel
Brenda Betancourt [aut],
Andee Kaplan [aut],
Ian Taylor [aut, cre]

Time series regression using dynamic linear models fit using MCMC.
BoomSpikeSlab, zoo, xts, Boom, R(>= 3.0.2)