Package: binspp 0.1.26

Remes Radim

binspp: 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) <arxiv:10.48550/arXiv.2205.07946>.

Authors:Mrkvicka Tomas [aut], Dvorak Jiri [aut], Beranek Ladislav [aut], Remes Radim [aut, cre]

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binspp.pdf |binspp.html
binspp/json (API)

# Install 'binspp' in R:
install.packages('binspp', repos = c('https://tomasmrkvicka.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/tomasmrkvicka/binspp/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

2.70 score 1 stars 279 downloads 9 exports 26 dependencies

Last updated 2 years agofrom:de6458c050. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-win-x86_64NOTENov 12 2024
R-4.5-linux-x86_64NOTENov 12 2024
R-4.4-win-x86_64NOTENov 12 2024
R-4.4-mac-x86_64NOTENov 12 2024
R-4.4-mac-aarch64NOTENov 12 2024
R-4.3-win-x86_64NOTENov 12 2024
R-4.3-mac-x86_64NOTENov 12 2024
R-4.3-mac-aarch64NOTENov 12 2024

Exports:estgtpestgtprestintpfirst_stepplot_outputsprint_outputsre_estimatergtprThomasInhom

Dependencies:abindclusterdeldirgoftestlatticeMatrixmgcvmvtnormnlmepolyclipRcppRcppArmadilloRcppEigenrpartspatstatspatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.univarspatstat.utilstensorVGAM