R packages

\(\texttt{randomMachines::}\)

This R package, available on CRAN as the \(\texttt{randomMachines}\) package, implements a novel ensemble method employing Support Vector Machines (SVMs) as base learners. This powerful ensemble model is designed for both classification (Ara et al., 2021) and regression (Ara et al., 2021) problems, offering versatility and robust performance across different datasets compared with other consolidated methods, such as Random Forests (Maia et al., 2021) .

To install it only need to run

install.packages(randomMachines)

\(\texttt{gpbart::}\)

A R implementation, built on \(\texttt{C++}\) and \(\texttt{Rcpp}\), extends the Bayesian Additive Regression Trees using Gaussian Processes. This extension was developed by myself, Keefe Murphy, and Andrew Parnell. Further details about the statistical model are presented in the paper titled: “GP-BART: A novel Bayesian additive regression trees approach using Gaussian processes,” published in the Computational & Data Analysis journal.

The repository can be found at: https://github.com/MateusMaiaDS/gpbart, and to install it, you only need to run:

devtools::install_github("MateusMaiaDS/gpbart")