./geography/R-spatstat.core, Core Functionality of the spatstat Family

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Branch: CURRENT, Version: 2.3.0, Package name: R-spatstat.core-2.3.0, Maintainer: pkgsrc-users

Functionality for data analysis and modelling of spatial data, mainly
spatial point patterns, in the 'spatstat' family of packages.
(Excludes analysis of spatial data on a linear network, which is
covered by the separate package 'spatstat.linnet'.) Exploratory
methods include quadrat counts, K-functions and their simulation
envelopes, nearest neighbour distance and empty space statistics, Fry
plots, pair correlation function, kernel smoothed intensity, relative
risk estimation with cross-validated bandwidth selection, mark
correlation functions, segregation indices, mark dependence
diagnostics, and kernel estimates of covariate effects. Formal
hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov,
Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte
Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson,
Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can
be fitted to point pattern data using the functions ppm(), kppm(),
slrm(), dppm() similar to glm(). Types of models include Poisson,
Gibbs and Cox point processes, Neyman-Scott cluster processes, and
determinantal point processes. Models may involve dependence on
covariates, inter-point interaction, cluster formation and dependence
on marks. Models are fitted by maximum likelihood, logistic
regression, minimum contrast, and composite likelihood methods. A
model can be fitted to a list of point patterns (replicated point
pattern data) using the function mppm(). The model can include random
effects and fixed effects depending on the experimental design, in
addition to all the features listed above. Fitted point process models
can be simulated, automatically. Formal hypothesis tests of a fitted
model are supported (likelihood ratio test, analysis of deviance,
Monte Carlo tests) along with basic tools for model selection
(stepwise(), AIC()) and variable selection (sdr). Tools for validating
the fitted model include simulation envelopes, residuals, residual
plots and Q-Q plots, leverage and influence diagnostics, partial
residuals, and added variable plots.


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   2021-10-26 12:45:18 by Nia Alarie | Files touched by this commit (108)
Log message:
geography: Replace RMD160 checksums with BLAKE2s checksums

All checksums have been double-checked against existing RMD160 and
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   2021-10-07 16:09:33 by Nia Alarie | Files touched by this commit (108)
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geography: Remove SHA1 hashes for distfiles
   2021-09-20 12:38:59 by Makoto Fujiwara | Files touched by this commit (3)
Log message:
(geography/R-spatstat.core) import R-spatstat.core-2.3.0

Functionality for data analysis and modelling of spatial data, mainly
spatial point patterns, in the 'spatstat' family of packages.
(Excludes analysis of spatial data on a linear network, which is
covered by the separate package 'spatstat.linnet'.) Exploratory
methods include quadrat counts, K-functions and their simulation
envelopes, nearest neighbour distance and empty space statistics, Fry
plots, pair correlation function, kernel smoothed intensity, relative
risk estimation with cross-validated bandwidth selection, mark
correlation functions, segregation indices, mark dependence
diagnostics, and kernel estimates of covariate effects. Formal
hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov,
Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte
Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson,
Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can
be fitted to point pattern data using the functions ppm(), kppm(),
slrm(), dppm() similar to glm(). Types of models include Poisson,
Gibbs and Cox point processes, Neyman-Scott cluster processes, and
determinantal point processes. Models may involve dependence on
covariates, inter-point interaction, cluster formation and dependence
on marks. Models are fitted by maximum likelihood, logistic
regression, minimum contrast, and composite likelihood methods. A
model can be fitted to a list of point patterns (replicated point
pattern data) using the function mppm(). The model can include random
effects and fixed effects depending on the experimental design, in
addition to all the features listed above. Fitted point process models
can be simulated, automatically. Formal hypothesis tests of a fitted
model are supported (likelihood ratio test, analysis of deviance,
Monte Carlo tests) along with basic tools for model selection
(stepwise(), AIC()) and variable selection (sdr). Tools for validating
the fitted model include simulation envelopes, residuals, residual
plots and Q-Q plots, leverage and influence diagnostics, partial
residuals, and added variable plots.