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spatstat
spatstat.random:Random Generation Functionality for the 'spatstat' Family
Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'.
Maintained by Adrian Baddeley. Last updated 20 hours ago.
point-processesrandom-generationsimulationspatial-samplingspatial-simulationcpp
5 stars 11.04 score 84 scripts 178 dependentsspatstat
spatstat.linnet:Linear Networks Functionality of the 'spatstat' Family
Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. 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 function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. 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. Random point patterns on a network can be generated using a variety of models.
Maintained by Adrian Baddeley. Last updated 2 months ago.
density-estimationheat-equationkernel-density-estimationnetwork-analysispoint-processesspatial-data-analysisstatistical-analysisstatistical-inferencestatistical-models
6 stars 9.58 score 35 scripts 42 dependents