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the latest development version of spatstat.model.
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- Overview of
spatstat.model - Detailed contents of package
- Installing the package
- Bug reports
- Questions
- Proposing changes to code
- Future development
The original spatstat package has been split into
several sub-packages
(see 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.
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)
For a full list of functions, see the help file for spatstat.model-package.
- 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
- 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
- residuals
- leverage
- influence
- partial residual plot
- added variable plot
- diagnostic plots
- pseudoscore residual plots
- model compensators of summary functions
- Q-Q plots
This repository contains the development version of
spatstat.model. The easiest way to install the development version
is to start R and type
repo <- c('https://spatstat.r-universe.dev', 'https://cloud.r-project.org')
install.packages("spatstat.model", dependencies=TRUE, repos=repo)To install the latest public release of spatstat.model,
type
install.packages("spatstat.model")Users are encouraged to report bugs.
If you find a bug in a spatstat function,
please identify the sub-package containing that function.
Visit the GitHub repository for the sub-package,
click the Issues tab at the top of the page,
and press new issue to start a new bug report, documentation correction
or feature request.
Please do not post questions on the Issues pages, because they are too clunky for correspondence.
For questions about the spatstat package family, first check
the question-and-answer website
stackoverflow
to see whether your question has already been asked and answered.
If not, you can either post your question at stackoverflow, or
email the authors.
Feel free to fork spatstat.model, make changes to the code,
and ask us to include them in the package by making a github pull request.
The spatstat package family is the result of 30 years of software development
and contains over 200,000 lines of code.
It is still under development,
motivated by the needs of researchers in many fields,
and driven by innovations in statistical science.
We welcome contributions of code, and suggestions
for improvements.