  In this article, we introduce the BNPqte R package which implements the
Bayesian nonparametric approach of Xu, Daniels and Winterstein (2018) for
estimating quantile treatment effects in observational studies. This approach
provides flexible modeling of the distributions of potential outcomes, so it is
capable of capturing a variety of underlying relationships among the outcomes,
treatments and confounders and estimating multiple quantile treatment effects
simultaneously. Specifically, this approach uses a Bayesian additive regression
trees (BART) model to estimate the propensity score and a Dirichlet process
mixture (DPM) of multivariate normals model to estimate the conditional
distribution of the potential outcome given the estimated propensity score. The
BNPqte R package provides a fast implementation for this approach by designing
efficient R functions for the DPM of multivariate normals model in joint and
conditional density estimation. These R functions largely improve the
efficiency of the DPM model in density estimation, compared to the popular
DPpackage. BART-related R functions in the BNPqte R package are inherited from
the BART R package with two modifications on variable importance and split
probability. To maximize computational efficiency, the actual sampling and
computation for each model are carried out in C++ code. The Armadillo C++
library is also used for fast linear algebra calculations.
