spatstat r-universe repositoryhttps://spatstat.r-universe.devPackage updated in spatstatcranlike-server 0.16.88https://github.com/spatstat.png?size=400spatstat r-universe repositoryhttps://spatstat.r-universe.devMon, 19 Feb 2024 09:10:46 GMT[spatstat] spatstat.geom 3.2-8.009Adrian.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/7957284970Mon, 19 Feb 2024 09:10:46 GMTspatstat.geom3.2-8.009successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.geom[spatstat] spatstat.linnet 3.1-4.001Adrian.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/7782170054Mon, 05 Feb 2024 08:47:53 GMTspatstat.linnet3.1-4.001successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.linnet[spatstat] spatstat.model 3.2-10.001Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Functionality for parametric statistical modelling and
inference for 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'.) Supports parametric
modelling, formal statistical inference, and model validation.
Parametric models include Poisson point processes, Cox point
processes, Neyman-Scott cluster processes, Gibbs point
processes and determinantal point processes. Models can be
fitted to data using maximum likelihood, maximum
pseudolikelihood, maximum composite likelihood and the method
of minimum contrast. Fitted models can be simulated and
predicted. Formal inference includes hypothesis tests (quadrat
counting tests, Cressie-Read tests, Clark-Evans test, Berman
test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised
permutation test, segregation test, ANOVA tests of fitted
models, adjusted composite likelihood ratio test, envelope
tests, Dao-Genton test, balanced independent two-stage test),
confidence intervals for parameters, and prediction intervals
for point counts. Model validation techniques include leverage,
influence, partial residuals, added variable plots, diagnostic
plots, pseudoscore residual plots, model compensators and Q-Q
plots.https://github.com/r-universe/spatstat/actions/runs/7782169812Mon, 05 Feb 2024 08:46:12 GMTspatstat.model3.2-10.001successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.model[spatstat] spatstat.random 3.2-2.001Adrian.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 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'.https://github.com/r-universe/spatstat/actions/runs/7782169189Mon, 05 Feb 2024 08:41:23 GMTspatstat.random3.2-2.001successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.random[spatstat] spatstat.explore 3.2-6.001Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Functionality for exploratory data analysis and
nonparametric analysis 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'.) 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.https://github.com/r-universe/spatstat/actions/runs/7782169608Mon, 05 Feb 2024 08:30:27 GMTspatstat.explore3.2-6.001successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.explore[spatstat] spatstat 3.0-7.002Adrian.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 3000 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/7772477968Sun, 04 Feb 2024 07:23:03 GMTspatstat3.0-7.002successhttps://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 05:41:232024-02-01 07:53:31datasets.Rnwdatasets.pdfDatasets Provided for the Spatstat Package2014-11-19 09:15:412023-02-01 05:49:45getstart.Rnwgetstart.pdfGetting Started with Spatstat2014-08-11 19:04:232021-12-12 10:08:34fv.Rnwfv.pdfGuide to Function Objects in Spatstat2023-07-21 05:53:522023-09-04 01:50:11updates.Rnwupdates.pdfSummary of Recent Updates to the Spatstat Family2014-08-11 19:36:462024-02-04 07:23:03[spatstat] spatstat.data 3.0-4Adrian.Baddeley@curtin.edu.au (Adrian Baddeley)Contains all the datasets for the 'spatstat' family of
packages.https://github.com/r-universe/spatstat/actions/runs/7897675778Mon, 15 Jan 2024 07:13:26 GMTspatstat.data3.0-4successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.data[spatstat] spatstat.utils 3.0-4Adrian.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/7853625800Tue, 24 Oct 2023 05:03:46 GMTspatstat.utils3.0-4successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.utils[spatstat] spatstat.sparse 3.0-3Adrian.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/7780670174Tue, 24 Oct 2023 05:00:01 GMTspatstat.sparse3.0-3successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.sparse[spatstat] spatstat.core 2.4-4.010Adrian.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/7868239039Tue, 24 May 2022 05:37:16 GMTspatstat.core2.4-4.010successhttps://spatstat.r-universe.devhttps://github.com/spatstat/spatstat.core[spatstat] RandomFieldsUtils 1.2.5schlather@math.uni-mannheim.de (Martin Schlather)Various utilities are provided that might be used in
spatial statistics and elsewhere. It delivers a method for
solving linear equations that checks the sparsity of the matrix
before any algorithm is used.https://github.com/r-universe/spatstat/actions/runs/7853625663Tue, 19 Apr 2022 10:32:32 GMTRandomFieldsUtils1.2.5successhttps://spatstat.r-universe.devhttps://github.com/cran/RandomFieldsUtils[spatstat] RandomFields 3.3.14schlather@math.uni-mannheim.de (Martin Schlather)Methods for the inference on and the simulation of
Gaussian fields are provided. Furthermore, methods for the
simulation of extreme value random fields are provided. Main
geostatistical parts are based among others on the books by
Christian Lantuejoul <doi:10.1007/978-3-662-04808-5>.https://github.com/r-universe/spatstat/actions/runs/7853625984Tue, 18 Jan 2022 18:12:52 GMTRandomFields3.3.14successhttps://spatstat.r-universe.devhttps://github.com/cran/RandomFields