https://github.com/spatstat/spatstat.model
Sub-package of spatstat containing functionality for parametric modelling and inference
https://github.com/spatstat/spatstat.model
analysis-of-variance cluster-process confidence-intervals cox-process determinantal-point-processes gibbs-process influence leverage model-diagnostics neyman-scott parameter-estimation poisson-process spatial-analysis spatial-modelling spatial-point-processes statistical-inference
Last synced: 5 months ago
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Sub-package of spatstat containing functionality for parametric modelling and inference
- Host: GitHub
- URL: https://github.com/spatstat/spatstat.model
- Owner: spatstat
- Created: 2022-05-23T06:12:38.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2025-09-28T02:49:12.000Z (6 months ago)
- Last Synced: 2025-09-28T04:21:29.206Z (6 months ago)
- Topics: analysis-of-variance, cluster-process, confidence-intervals, cox-process, determinantal-point-processes, gibbs-process, influence, leverage, model-diagnostics, neyman-scott, parameter-estimation, poisson-process, spatial-analysis, spatial-modelling, spatial-point-processes, statistical-inference
- Language: R
- Homepage:
- Size: 2.18 MB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS
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README
# spatstat.model
## Parametric statistical modelling of spatial data for the spatstat family
[](http://CRAN.R-project.org/package=spatstat.model)
[](https://github.com/spatstat/spatstat.model)
The original `spatstat` package has been split into
several sub-packages (See [spatstat/spatstat](https://github.com/spatstat/spatstat))
This package `spatstat.model` is one of the
sub-packages. It contains all the main user-level functions that perform
**parametric statistical modelling** of spatial data,
with the exception of data on linear networks.
Most of the functionality is for spatial point patterns in two dimensions.
There is a very modest amount of functionality for 3D and higher dimensional patterns
and space-time patterns.
### Overview
`spatstat.model` supports
- parametric modelling (fitting models to point pattern data, model selection, model prediction)
- formal inference (hypothesis tests, confidence intervals)
- informal validation (model diagnostics)
### Detailed contents
For a full list of functions, see the help file for `spatstat.model-package`.
#### Parametric modelling
- fitting Poisson point process models to point pattern data (`ppm`)
- fitting spatial logistic regression models to point pattern data (`slrm`)
- fitting Cox point process models to point pattern data (`kppm`)
- fitting Neyman-Scott cluster process models to point pattern data (`kppm`)
- fitting Gibbs point process models to point pattern data (`ppm`)
- fitting determinantal point process models to point pattern data (`dppm`)
- fitting recursively partitioned models to point patterns (`rppm`)
- class support for fitted models (`update`, `print`, `summary`, `predict`, `plot`, `simulate`, `coef`, `confint`, `vcov`, `anova`, `residuals`, `fitted`, `deviance`, `AIC`, `logLik`, `terms`, `formula`, `model.matrix`)
- minimum contrast estimation (generic algorithm)
- simulation of fitted point process models
#### Formal inference
- hypothesis tests (quadrat test, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite
likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test)
- confidence intervals for parameters of a model
- prediction intervals for point counts
#### Informal validation
- residuals
- leverage
- influence
- partial residual plot
- added variable plot
- diagnostic plots
- pseudoscore residual plots
- model compensators of summary functions
- Q-Q plots