binspp {binspp}R Documentation

Bayesian inference for Neyman-Scott point processes


The Bayesian MCMC estimation of parameters for Thomas-type cluster point process with various inhomogeneities. It allows for inhomogeneity in (i) distribution of parent points, (ii) mean number of points in a cluster, (iii) cluster spread. The package also allows for the Bayesian MCMC algorithm for the homogeneous generalized Thomas process. The cluster size is allowed to have a variance that is greater or less than the expected value (cluster sizes are over or under dispersed). Details are described in Dvořák, Remeš, Beránek & Mrkvička (2022) (doi:10.48550/arXiv.2205.07946).


License: GPL-3


Tomas Mrkvicka <> (author), Jiri Dvorak <> (author), Ladislav Beranek <> (author), Radim Remes <> (author, creator)


Anderson, C. Mrkvička T. (2020). Inference for cluster point processes with over- or under-dispersed cluster sizes, Statistics and computing 30, 1573–1590, doi:10.1007/s11222-020-09960-8.

Kopecký J., Mrkvička T. (2016). On the Bayesian estimation for the stationary Neyman-Scott point processes, Applications of Mathematics 61/4, 503-514. Available from:

Dvořák, J., Remeš, R., Beránek, L., Mrkvička, T. (2022). binspp: An R Package for Bayesian Inference for Neyman-Scott Point Processes with Complex Inhomogeneity Structure. arXiv. doi:10.48550/ARXIV.2205.07946.

[Package binspp version 0.1.26 Index]