![[BACK]](/icons/back.gif){kind=icon}
Up to [cvs.NetBSD.org] / pkgsrc / geography / R-spatstat.core
Request diff between arbitrary revisions
Keyword substitution: kv
Default branch: MAIN
(geography/R-spatstat.core) Removed
In https://cran.r-project.org/web/packages/spatstat/NEWS
CHANGES IN spatstat VERSION 3.0-1
SIGNIFICANT USER-VISIBLE CHANGES
o Package structure
The package 'spatstat.core' has been split into two packages called
'spatstat.explore' (for exploratory data analysis)
and 'spatstat.model' (for modelling and formal inference).
o Package dependence
'spatstat' now depends on the new packages 'spatstat.explore'
and 'spatstat.model' which have replaced the old package 'spatstat.core'.
geography: Replace RMD160 checksums with BLAKE2s checksums All checksums have been double-checked against existing RMD160 and SHA512 hashes
geography: Remove SHA1 hashes for distfiles
(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.