  Bayesian additive regression tree (BART) models have seen increased attention
in recent years as a general-purpose nonparametric modeling technique. BART
combines the flexibility of modern machine learning techniques with the
principled uncertainty quantification of Bayesian inference, and it has been
shown to be uniquely appropriate for addressing the high-noise problems that
occur commonly in many areas of science, including medicine and the social
sciences. This paper introduces the SoftBart package for fitting the Soft BART
algorithm of Linero and Yang (2018). In addition to improving upon the
predictive performance of other BART packages, a major goal of this package has
been to facilitate the inclusion of BART in larger models, making it ideal for
researchers in Bayesian statistics. I show both how to use this package for
standard prediction tasks and how to embed BART models in larger models; I
illustrate by using SoftBart to implement a nonparametric probit regression
model, a semiparametric varying coefficient model, and a partial linear model.
