Barnard's unconditional test for 2x2 contingency tables.

Calculate the bark beetle phenology based on raster data or
    point-related data. There are multiple models implemented for two bark
    beetle species. The models can be customized and their submodels (onset of
    infestation, beetle development, diapause initiation, mortality) can be
    combined. The following models are available in the package:
    PHENIPS-Clim (first-time release in this package),
    PHENIPS (Baier et al. 2007) <doi:10.1016/j.foreco.2007.05.020>,
    RITY (Ogris et al. 2019) <doi:10.1016/j.ecolmodel.2019.108775>,
    CHAPY (Ogris et al. 2020) <doi:10.1016/j.ecolmodel.2020.109137>,
    BSO (Jakoby et al. 2019) <doi:10.1111/gcb.14766>,
    Lange et al. (2008) <doi:10.1007/978-3-540-85081-6_32>,
    Jönsson et al. (2011) <doi:10.1007/s10584-011-0038-4>.
    The package may be expanded by models for other bark beetle species in the
    future.

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>.