Package: binspp 0.1.26
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:
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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')) |
Bug tracker:https://github.com/tomasmrkvicka/binspp/issues
- cov_refor - Distance to the reforestration polygon
- cov_reserv - Distance to the reservoir
- cov_slope - Slope of the area
- cov_tdensity - Trees density
- cov_tmi - Topographic moisture index
- trees_N4 - Spanish oak trees
- x_left_N4 - Left horizontal corners for trees_N4 dataset
- x_right_N4 - Right horizontal corners for trees_N4 dataset
- y_bottom_N4 - Bottom vertical corners for trees_N4 dataset
- y_top_N4 - Vertical corners for trees_N4 dataset
Last updated 2 years agofrom:de6458c050. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 12 2024 |
R-4.5-win-x86_64 | NOTE | Nov 12 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 12 2024 |
R-4.4-win-x86_64 | NOTE | Nov 12 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 12 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 12 2024 |
R-4.3-win-x86_64 | NOTE | Nov 12 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 12 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 12 2024 |
Exports:estgtpestgtprestintpfirst_stepplot_outputsprint_outputsre_estimatergtprThomasInhom
Dependencies:abindclusterdeldirgoftestlatticeMatrixmgcvmvtnormnlmepolyclipRcppRcppArmadilloRcppEigenrpartspatstatspatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.univarspatstat.utilstensorVGAM