QuantLib library (>= 1.14) from
https://quantlib.org, Boost library from https://www.boost.org

methods, Rcpp (>= 0.11.0), stats, graphics

Dirk Eddelbuettel, Khanh Nguyen (during 2009-2010), Terry Leitch (2016)
QuantLib library (>= 1.7.0) from
http://quantlib.org, Boost library from http://www.boost.org

QuantLib library (>= 1.8.0) from
http://quantlib.org, Boost library from http://www.boost.org

c(person("Xiaomeng", "Ju", role=c("aut", "cre"), email="xiaomeng.ju@stat.ubc.ca"),
person("Matias", "Salibian-Barrera", role = c("aut"), email = "matias@stat.ubc.ca")
)
An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065> (to appear in Computational Statistics and Data Science). It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <doi:10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <doi:10.1016/j.csda.2007.11.006>).
Xiaomeng Ju [aut, cre],
Matias Salibian-Barrera [aut]

Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener, Regularized random forest by Houtao Deng.
Feature Selection with Regularized Random Forest. This package is based on the 'randomForest' package by Andy Liaw. The key difference is the RRF function that builds a regularized random forest.

Fortran original by Leo Breiman and Adele Cutler, R port by
Andy Liaw and Matthew Wiener, Regularized random forest by
Houtao Deng.
Feature Selection with Regularized Random Forest. This
package is based on the 'randomForest' package by Andy Liaw.
The key difference is the RRF function that builds a
regularized random forest.

Fortran original by Leo Breiman and Adele Cutler, R port by
Andy Liaw and Matthew Wiener, Regularized random forest for classification by
Houtao Deng. Regularized random forest for regression by Xin Guan.
Feature Selection with Regularized Random Forest. This
package is based on the 'randomForest' package by Andy Liaw.
The key difference is the RRF() function that builds a
regularized random forest.