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math/Rspatstat.linnet,
Linear Networks Functionality of the spatstat Family
Branch: CURRENT,
Version: 2.3.0,
Package name: Rspatstat.linnet2.3.0,
Maintainer: pkgsrcusersDefines 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,
Kfunctions and pair correlation functions on a network, simulation
envelopes, nearest neighbour distance and empty space distance,
relative risk estimation with crossvalidated bandwidth selection.
Formal hypothesis tests of random pattern (chisquared,
KolmogorovSmirnov, Monte Carlo, DiggleCressieLoosmoreFord,
DaoGenton, twostage Monte Carlo) and tests for covariate effects
(CoxBermanWallerLawson, KolmogorovSmirnov, 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 QQ 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.
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 (20210920) Package added to pkgsrc.se, version Rspatstat.linnet2.3.0 (created)
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20211026 12:45:18 by Nia Alarie  Files touched by this commit (108) 
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20211007 16:09:33 by Nia Alarie  Files touched by this commit (108) 
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20210920 12:45:32 by Makoto Fujiwara  Files touched by this commit (3) 
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(geography/Rspatstat.linnet) import Rspatstat.linnet2.3.0
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,
Kfunctions and pair correlation functions on a network, simulation
envelopes, nearest neighbour distance and empty space distance,
relative risk estimation with crossvalidated bandwidth selection.
Formal hypothesis tests of random pattern (chisquared,
KolmogorovSmirnov, Monte Carlo, DiggleCressieLoosmoreFord,
DaoGenton, twostage Monte Carlo) and tests for covariate effects
(CoxBermanWallerLawson, KolmogorovSmirnov, 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 QQ 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.
