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) (tools:::Rd_expr_doi("10.48550/arXiv.2205.07946") ). package
License: GPL-3
Anderson, C. Mrkvička T. (2020). Inference for cluster point processes with over- or under-dispersed cluster sizes, Statistics and computing 30 , 1573–1590, tools:::Rd_expr_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. tools:::Rd_expr_doi("10.48550/ARXIV.2205.07946") .
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)