spatstat r-universe repositoryhttps://spatstat.r-universe.devPackage updated in spatstatcranlike-server 0.11.5https://github.com/spatstat.png?size=400spatstat r-universe repositoryhttps://spatstat.r-universe.devSun, 22 May 2022 00:24:39 GMT[spatstat] spatstat.linnet 2.3-2.015Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)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.https://github.com/r-universe/spatstat/actions/runs/2364890396Sun, 22 May 2022 00:24:39 GMTspatstat.linnet2.3-2.015successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.linnet[spatstat] spatstat.random 2.2-0.005Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)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
(simple sequential inhibition, Matern inhibition models, Matern cluster process,
Neyman-Scott cluster processes, log-Gaussian Cox processes, product shot noise cluster processes)
and simulation of Gibbs point processes (Metropolis-Hastings birth-death-shift algorithm,
alternating Gibbs sampler). 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'.https://github.com/r-universe/spatstat/actions/runs/2364890494Sun, 22 May 2022 00:23:14 GMTspatstat.random2.2-0.005successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.random[spatstat] spatstat.geom 2.4-0.021Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Defines spatial data types and supports geometrical operations
on them. Data types include point patterns, windows (domains),
pixel images, line segment patterns, tessellations and hyperframes.
Capabilities include creation and manipulation of data
(using command line or graphical interaction),
plotting, geometrical operations (rotation, shift, rescale,
affine transformation), convex hull, discretisation and
pixellation, Dirichlet tessellation, Delaunay triangulation,
pairwise distances, nearest-neighbour distances,
distance transform, morphological operations
(erosion, dilation, closing, opening), quadrat counting,
geometrical measurement, geometrical covariance,
colour maps, calculus on spatial domains,
Gaussian blur, level sets of images, transects of images,
intersections between objects, minimum distance matching.
(Excludes spatial data on a network, which are supported by
the package 'spatstat.linnet'.)https://github.com/r-universe/spatstat/actions/runs/2364890299Sun, 22 May 2022 00:04:49 GMTspatstat.geom2.4-0.021successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.geom[spatstat] spatstat.core 2.4-4.009Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)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.https://github.com/r-universe/spatstat/actions/runs/2362679937Sat, 21 May 2022 09:10:49 GMTspatstat.core2.4-4.009successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.core[spatstat] spatstat 2.3-4.002Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images.
Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference.
Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks.
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.https://github.com/r-universe/spatstat/actions/runs/2314060845Sat, 07 May 2022 07:41:40 GMTspatstat2.3-4.002successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstatreplicated.Rnwreplicated.pdfAnalysing Replicated Point Patterns in Spatstat2014-08-11 19:34:102021-12-12 10:08:34bugfixes.Rnwbugfixes.pdfBugs Fixed in Spatstat2018-07-27 05:41:232022-04-05 08:31:41datasets.Rnwdatasets.pdfDatasets Provided for the Spatstat Package2014-11-19 09:15:412021-12-12 10:08:34getstart.Rnwgetstart.pdfGetting Started with Spatstat2014-08-11 19:04:232021-12-12 10:08:34shapefiles.Rnwshapefiles.pdfHandling shapefiles in the spatstat package2014-08-11 19:04:232021-12-12 10:08:34updates.Rnwupdates.pdfSummary of Recent Updates to the Spatstat Family2014-08-11 19:36:462022-03-31 03:09:45[spatstat] spatstat.sparse 2.1-1.001Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Defines sparse three-dimensional arrays
and supports standard operations on them.
The package also includes utility functions for
matrix calculations that are common in
statistics, such as quadratic forms.https://github.com/r-universe/spatstat/actions/runs/2314058960Fri, 06 May 2022 07:30:03 GMTspatstat.sparse2.1-1.001successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.sparse[spatstat] spatstat.data 2.2-0.002Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Contains all the datasets for the 'spatstat' family of packages.https://github.com/r-universe/spatstat/actions/runs/2315370107Fri, 06 May 2022 07:28:29 GMTspatstat.data2.2-0.002successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.data[spatstat] spatstat.utils 2.3-1Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Contains utility functions for the 'spatstat' family of packages
which may also be useful for other purposes.https://github.com/r-universe/spatstat/actions/runs/2315369888Fri, 06 May 2022 07:20:34 GMTspatstat.utils2.3-1successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.utils