Title: Multivariate Function Visualization and Smooth Multiband Color
Description: Combined contour/heatmap plots, 3d bar/surface plots, isosurface plots, triangular plots and 2d/3d vector fields. Builds on the colorspace package (Zeileis, A., et al. (2019) <arxiv.org/abs/1903.06490>), by supporting smooth multiband color interpolation, in sRGB, HSV and HCL color spaces. The default color functions for heatmaps, use HCL (Zeileis, A., et al. (2009) <doi:10.1016/j.csda.2008.11.033>) and support near-uniform perception.
Contact: Primary <spurdle.a@gmail.com>, Secondary <spurdle.a@aol.com>

Title: Contour Plots, 3D Plots, Vector Fields and Heatmaps
Description: 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.

Title: Bayesian Additive Regression Trees using Bayesian Model
Description: "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.
Imports: Rcpp (>= 1.0.0), mvnfast, Rdpack