Author: Dirk Eddelbuettel, Khanh Nguyen (2009-2010), Terry Leitch (since 2016)
The 'QuantLib' project aims to provide a comprehensive software framework
for quantitative finance. The goal is to provide a standard open source library
for quantitative analysis, modeling, trading, and risk management of financial
Imports: methods, Rcpp (>= 0.11.0), stats, graphics, zoo
SystemRequirements: QuantLib library (>= 1.14) from

https://quantlib.org, Boost library from https://www.boost.org

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

Author: Dirk Eddelbuettel, Khanh Nguyen (during 2009-2010), Terry Leitch (2016)
SystemRequirements: QuantLib library (>= 1.7.0) from

SystemRequirements: QuantLib library (>= 1.8.0) from

Authors@R: 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")
Description: 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>).
Packaged: 2020-10-05 16:10:45 UTC; JasmineJu

Author: Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener, Regularized random forest by Houtao Deng.
Description: 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.

Author: Fortran original by Leo Breiman and Adele Cutler, R port by
Andy Liaw and Matthew Wiener, Regularized random forest by
Description: 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