binspp: Bayesian inference for Neyman-Scott point processes

In binspp: Bayesian Inference for Neyman-Scott Point Processes

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) (\Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2205.07946")}).

Note

License: GPL-3

Author(s)

Tomas Mrkvicka <[e-mail]> (author), Jiri Dvorak <[e-mail]> (author), Ladislav Beranek <[e-mail]> (author), Radim Remes <[e-mail]> (author, creator), Jaewoo Park <[e-mail]> (contributor), Sujeong Lee <[e-mail]> (contributor)

References

Anderson, C. Mrkvička T. (2020). Inference for cluster point processes with over- or under-dispersed cluster sizes, Statistics and computing 30, 1573–1590, \Sexpr[results=rd]{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: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10492-016-0144-8")}.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/ARXIV.2205.07946")}.

binspp documentation built on June 8, 2025, 1:50 p.m.