Package: binspp 0.2.4

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], Park Jaewoo [ctb], Lee Sujeong [ctb]

binspp_0.2.4.tar.gz
binspp_0.2.4.tar.gz(r-4.7-arm64)binspp_0.2.4.tar.gz(r-4.7-x86_64)binspp_0.2.4.tar.gz(r-4.6-arm64)binspp_0.2.4.tar.gz(r-4.6-x86_64)
binspp_0.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
binspp/json (API)

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

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

openblascppopenmp

2.30 score 6 scripts 304 downloads 13 exports 32 dependencies

Last updated from:dfa48d2b0f. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK199
linux-devel-x86_64OK203
source / vignettesOK419
linux-release-arm64OK186
linux-release-x86_64OK208
wasm-releaseOK144

Exports:AuxVarGencoeffestgtpestgtprestinternspestintpfirst_steppCClik2plot_connrawMCMCoutputre_estimatergtprThomasInhom

Dependencies:abindclusterdeldirdotCall64fieldsgoftestlatticemapsMatrixmgcvmvtnormnlmepolyclipRColorBrewerRcppRcppArmadilloRcppEigenrpartspamspatstatspatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.univarspatstat.utilstensorVGAMviridisLite

Bayesian inference for Neyman-Scott point processes with complex inhomogeneity structure

Rendered frombinspp.Rmdusingknitr::rmarkdownon Jun 16 2026.

Last update: 2026-06-16
Started: 2026-06-16

Readme and manuals

Help Manual

Help pageTopics
Generate auxiliary variable for given proposed parameters.AuxVarGen
Bayesian inference for Neyman-Scott point processesbinspp
Calculate parameters for Birth and Death Interaction likelihood functions.coeff
Distance to the reforestration polygoncov_refor
Distance to the reservoircov_reserv
Slope of the areacov_slope
Trees densitycov_tdensity
Topographic moisture indexcov_tmi
Auxiliary function which calculates sum values Bayesian MCMC estimation of parameters of generalized Thomas processestgtp
Results for Bayesian MCMC estimation of parameters of generalized Thomas processestgtpr
Estimation of interaction Neyman-Scott point process using auxiliary variable algorithm into Markov chain Monte Carlo.estinternsp
Estimation of Thomas-type cluster point process with complex inhomogeneitiesestintp
Estimate the first-order inhomogeneityfirst_step
Evaluate unnormalized likelihood for auxiliary variablepCClik2
plot_connplot_conn
Graphical output describing the posterior distributionsplot.output_estintp
Text output describing the posterior distributionsprint.output_estinternsp
Text output describing the posterior distributionsprint.output_estintp
Obtaining the raw MCMC outputrawMCMCoutput
Re-estimate the posterior distributions with a different burn-in or a different credibility level.re_estimate
Simulation of generalized Thomas processrgtp
Simulate a realization of Thomas-type cluster point process with complex inhomogeneitiesrThomasInhom
Simulation from the fitted modelsimulate.output_estintp
Spanish oak treestrees_N4
Left horizontal corners for trees_N4 datasetx_left_N4
Right horizontal corners for trees_N4 datasetx_right_N4
Bottom vertical corners for trees_N4 datasety_bottom_N4
Vertical corners for trees_N4 datasety_top_N4