binspp v0.2.4

Bayesian inference for Neyman-Scott point processes (R package)

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

R software with VGAM, spatstat, FNN, cluster and fields libraries installed. Additional required packages are: Rcpp, RcppArmadillo and RcppEigen.

In R shell write:

> install.packages(c("VGAM", "spatstat", "FNN", "cluster", "fields"))
> install.packages(c("Rcpp", "RcppArmadillo", "RcppEigen"))

Installing

Install package by downloading from CRAN:

install.packages("binspp")

You can also download binspp.tar.gz package and install it to your R software.

install.packages("C:/path/to/directory/binspp.tar.gz", 
    repos = NULL, 
    lib = "C:/path/to/libraryDirectory")

Running the tests

Load data dataset_N4.Rdata, run example scripts to test package functionality.

Built With

R Studio or any other R software.

• RStudio - The R Studio

Versioning

We use GitHub for versioning. For the versions available, see the binspp. You can also get binspp package on the CRAN.

Authors

• Tomas Mrkvicka - creator, author - ResearchGate
• Jiri Dvorak - author - ResearchGate
• Ladislav Beranek - author - GitHub
• Radim Remes - author, maintainer - GitHub
• Jaewoo Park - contributor - GitHub
• Sujeong Lee - contributor - GitHub

See also the list of contributors who participated in this project.

License

This project is licensed under the GNU GPL 3 License - see the LICENSE file for details

Acknowledgments

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