bendr: Built Environment Nested Dirichlet Processes in R


This is an R package that fits the Nested Dirichlet Process to grouped distance data according to an Inhomogenous Poisson Process model. The primary target audience is researchers interested in the effect of built environment features (BEFs) on human health, though other applications are possible. See the package’s website for an introduction. Currently both normal and beta base measures are implemented. See the documentation for more information.


Development Version

Currently this package is only available via Github. In order to install the software use the following lines of R code


install_github("apeterson91/bendr",dependencies = TRUE)


Examples and code contributions are welcome. Feel free to start/address a feature in the issue tracker and I’ll be notified shortly.

Code of Conduct

Please note that bendr is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

How to cite this package

The software can be cited using the first citation. The second citation refers to a paper which uses the software.

        author = {Peterson, Adam},
        title = { {bendr}: Built Environment Nested Dirichlet Processes},
        year = {2020},
        howpublished = {\url{}},
        note = {{R} package version 1.0.4}
      title={How Close and How Much? Linking Health Outcomes to Built Environment Spatial Distributions}, 
      author={Adam Peterson and Veronica Berrocal and Emma Sanchez-Vaznaugh and Brisa Sanchez},


This work was developed with support from NIH grant R01-HL131610 (PI: Sanchez).

Special thanks to Emily Hector and Andrew Whiteman for help with the package name.