Contour Plots, 3D Plots, Vector Fields and Heatmaps
Combined contour/heatmap plots, 3d bar/surface plots, 2d/3d triangular plots, isosurface plots and 2d/3d vector fields. By default, uses vector graphics, but it's possible to use raster graphics for regular heatmaps. Builds on the colorspace package (Zeileis, et al., 2020 <doi:10.18637/jss.v096.i01>), by supporting smooth multiband color interpolation, in sRGB, HSV and HCL color spaces.

Bayesian Additive Regression Trees using Bayesian Model
Averaging
Belinda Hernandez [aut, cre]
Adrian E. Raftery [aut]
Stephen R Pennington [aut]
Andrew C. Parnell [aut]
Eoghan O'Neill [ctb]
"BART-BMA Bayesian Additive Regression Trees using Bayesian Model Averaging" (Hernandez B, Raftery A.E., Parnell A.C. (2018) <doi:10.1007/s11222-017-9767-1>) is an extension to the original BART sum-of-trees model (Chipman et al 2010). BART-BMA differs to the original BART model in two main aspects in order to implement a greedy model which
will be computationally feasible for high dimensional data. Firstly BART-BMA uses a greedy search for the best split points and variables when growing decision trees within each sum-of-trees
model. This means trees are only grown based on the most predictive set of split rules. Also rather than using Markov chain Monte Carlo (MCMC), BART-BMA uses a greedy implementation of Bayesian Model Averaging called Occam's Window
which take a weighted average over multiple sum-of-trees models to form its overall prediction. This means that only the set of sum-of-trees for which there is high support from the data
are saved to memory and used in the final model.

Causal Inference using Bayesian Additive Regression Trees
c(
person("Vincent", "Dorie", role = c("aut", "cre"), email = "vdorie@gmail.com",      comment = c(ORCID = "0000-0002-9576-3064")),
person("Jennifer", "Hill", role = "aut",           email = "jennifer.hill@nyu.edu", comment = c(ORCID = "0000-0003-4983-2206")))
dbarts (>= 0.9-16), methods, stats, graphics, parallel, utils,
grDevices
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>).