| binspp {binspp} | R Documentation |
Bayesian inference for Neyman-Scott point processes
Description
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).
Note
License: GPL-3
Author(s)
Tomas Mrkvicka <mrkvicka.toma@gmail.com> (author), Jiri Dvorak <dvorak@karlin.mff.cuni.cz> (author), Ladislav Beranek <beranek@jcu.cz> (author), Radim Remes <inrem@jcu.cz> (author, creator)
References
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: https://am.math.cas.cz/am61-4/9.html.
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.