Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes.  For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

Contains a variety of methods to generate typical causal inference estimates using Bayesian Additive Regression Trees (BART) as the underlying regression model (Hill (2012) <doi:10.1198/jcgs.2010.08162>).

Fit Bayesian Regression Additive Trees (BART) models to
    select true confounders from a large set of potential confounders and
    to estimate average treatment effect. For more information, see Kim et
    al. (2023) <doi:10.1111/biom.13833>.

An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.

These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine.

Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits.  We construct conventional plots to analyze a model’s performance and stability  as well as create new tree-based plots to analyze variable importance, interaction, and tree structure.  We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance  and interactions jointly using colour scale to represent posterior uncertainty.  Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>,  'dbarts'  (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>,  and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.

Intended to facilitate acoustic analysis of (animal) sound transmission experiments, which typically aim to quantify changes in signal structure when transmitted in a given habitat by broadcasting and re-recording animal sounds at increasing distances. The package offers a workflow with functions to prepare the data set for analysis as well as to calculate and visualize several degradation metrics, including blur ratio, signal-to-noise ratio, excess attenuation and envelope correlation among others (Dabelsteen et al 1993 <doi:10.1121/1.406682>).