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No packages found for: spatstat

Commit Package Version Maintainer Src R-4.3.x Build System Dependencies
2023-12-09spatstat.explore3.2-5.007Adrian
Baddeley2023-12-092023-12-08spatstat.geom3.2-7.001Adrian
Baddeley2023-12-082023-12-01spatstat.linnet3.1-3.003Adrian
Baddeley2023-12-012023-11-29spatstat.random3.2-2Adrian Baddeley2023-11-29
c++ (12.3.0)
2023-10-30spatstat3.0-7Adrian
Baddeley2023-11-292023-10-24spatstat.data3.0-3Adrian
Baddeley2023-11-232023-10-24spatstat.utils3.0-4Adrian
Baddeley2023-11-232023-10-24spatstat.sparse3.0-3Adrian
Baddeley2023-11-272023-10-23spatstat.model3.2-8Adrian
Baddeley2023-11-222022-05-24spatstat.core2.4-4.010Adrian
Baddeley2023-11-302022-04-19RandomFieldsUtils1.2.5Martin Schlather2023-11-27
c++ (12.3.0) openblas (0.3.20) openmp (12.3.0)
2022-01-18RandomFields3.3.14Martin Schlather2023-12-02
c++ (12.3.0) openblas (0.3.20) openmp (12.3.0)

SPATSTAT.EXPLORE: EXPLORATORY DATA ANALYSIS FOR THE 'SPATSTAT' FAMILY

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.

Last updated 5 days ago

cluster-detectionconfidence-intervalshypothesis-testingk-functionroc-curvesscan-statisticssignificance-testingsimulation-envelopesspatial-analysisspatial-data-analysisspatial-sharpeningspatial-smoothingspatial-statistics

8.03 score 13 dependencies
 * 
 * Adrian Baddeley
   

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.

Last updated 12 days ago

density-estimationheat-equationkernel-density-estimationnetwork-analysispoint-processesspatial-data-analysisstatistical-analysisstatistical-inferencestatistical-models

6 stars 6.00 score 17 dependencies
 * 
 * Adrian Baddeley
   

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'.

Last updated 15 days ago

point-processesrandom-generationsimulationspatial-samplingspatial-simulationc++

4 stars 8.36 score 7 dependencies
 * 
 * Adrian Baddeley
   

SPATSTAT.DATA: DATASETS FOR 'SPATSTAT' FAMILY

Contains all the datasets for the 'spatstat' family of packages.

Last updated 2 months ago

kernel-densitypoint-processspatial-analysisspatial-dataspatial-data-analysisspatstatstatistical-analysisstatistical-methodsstatistical-testsstatistics

5 stars 9.13 score 3 dependencies
 * 
 * Adrian Baddeley
   

SPATSTAT.SPARSE: SPARSE THREE-DIMENSIONAL ARRAYS AND LINEAR ALGEBRA UTILITIES

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.

Last updated 2 months ago

arrayssparse-matrixsparse-representations

2 stars 7.99 score 5 dependencies
 * 
 * Adrian Baddeley
   

SPATSTAT.CORE: CORE FUNCTIONALITY OF THE 'SPATSTAT' FAMILY

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.

Last updated 2 years ago

model-checkingpoint-pattern-analysispoint-processrandom-generationrandom-samplingspatial-analysisspatial-dataspatstatstatistical-analysisstatistical-diagnosticsstatistical-inferencestatistical-modelsstatistical-tests

6 stars 1.08 score 15 dependencies
 * 
 * Adrian Baddeley
   

RANDOMFIELDS: SIMULATION AND ANALYSIS OF RANDOM FIELDS

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>.

Last updated 2 years ago

openblasc++openmp

6 stars 0.71 score 3 dependencies
 * 
 * Martin Schlather
   

SPATSTAT.GEOM: GEOMETRICAL FUNCTIONALITY OF THE 'SPATSTAT' FAMILY

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'.)

Last updated 5 days ago

classes-and-objectsdistance-calculationgeometrygeometry-processingimagesmensurationplottingpoint-patternsspatial-dataspatial-data-analysis

7 stars 9.15 score 6 dependencies
 * 
 * Adrian Baddeley
   

SPATSTAT: SPATIAL POINT PATTERN ANALYSIS, MODEL-FITTING, SIMULATION, TESTS

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.

Last updated 2 months ago

cluster-processcox-point-processgibbs-processkernel-densitynetwork-analysispoint-processpoisson-processspatial-analysisspatial-dataspatial-data-analysisspatial-statisticsspatstatstatistical-methodsstatistical-modelsstatistical-testsstatistics

172 stars 7.01 score 18 dependencies
 * 
 * Adrian Baddeley
   

SPATSTAT.UTILS: UTILITY FUNCTIONS FOR 'SPATSTAT'

Contains utility functions for the 'spatstat' family of packages which may also
be useful for other purposes.

Last updated 2 months ago

spatial-analysisspatial-dataspatstat

5 stars 9.18 score 0 dependencies
 * 
 * Adrian Baddeley
   

SPATSTAT.MODEL: PARAMETRIC STATISTICAL MODELLING AND INFERENCE FOR THE
'SPATSTAT' FAMILY

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.

Last updated 2 months ago

analysis-of-variancecluster-processconfidence-intervalscox-processdeterminantal-point-processesgibbs-processinfluenceleveragemodel-diagnosticsneyman-scottparameter-estimationpoisson-processspatial-analysisspatial-modellingspatial-point-processesstatistical-inference

4 stars 6.11 score 16 dependencies
 * 
 * Adrian Baddeley
   

RANDOMFIELDSUTILS: UTILITIES FOR THE SIMULATION AND ANALYSIS OF RANDOM FIELDS
AND GENETIC DATA

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.

Last updated 2 years ago

openblasc++openmp

0.71 score 0 dependencies
 * 
 * Martin Schlather
   

No packages found for: spatstat

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PACKAGES API

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Information about packages in this universe
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RandomFieldsRandomFieldsUtilsspatstatspatstat.corespatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.utils

DATASETS API

Download
Export datasets to various formats
spatstat.data::Kovesi (hyperframe)spatstat.data::amacrine
(ppp)spatstat.data::anemones (ppp)spatstat.data::ants
(ppp)spatstat.data::ants.extra (list)spatstat.data::austates
(tess)spatstat.data::bdspots (ppplist)spatstat.data::bei
(ppp)spatstat.data::bei.extra (imlist)spatstat.data::betacells
(ppp)spatstat.data::bramblecanes (ppp)spatstat.data::bronzefilter
(ppp)spatstat.data::btb (ppp)spatstat.data::btb.extra
(ppplist)spatstat.data::cells (ppp)spatstat.data::cetaceans
(hyperframe)spatstat.data::cetaceans.extra (list)spatstat.data::chicago
(lpp)spatstat.data::chorley (ppp)spatstat.data::chorley.extra
(list)spatstat.data::clmfires (ppp)spatstat.data::clmfires.extra
(list)spatstat.data::concrete (ppp)spatstat.data::copper
(list)spatstat.data::demohyper (hyperframe)spatstat.data::demopat
(ppp)spatstat.data::dendrite (lpp)spatstat.data::finpines
(ppp)spatstat.data::flu (hyperframe)spatstat.data::ganglia
(ppp)spatstat.data::gordon (ppp)spatstat.data::gorillas
(ppp)spatstat.data::gorillas.extra (imlist)spatstat.data::hamster
(ppp)spatstat.data::heather (solist)spatstat.data::humberside
(ppp)spatstat.data::humberside.convex (ppp)spatstat.data::hyytiala
(ppp)spatstat.data::japanesepines (ppp)spatstat.data::lansing
(ppp)spatstat.data::letterR (owin)spatstat.data::longleaf
(ppp)spatstat.data::mucosa (ppp)spatstat.data::mucosa.subwin
(owin)spatstat.data::murchison (solist)spatstat.data::nbfires
(ppp)spatstat.data::nbfires.extra (solist)spatstat.data::nbw.rect
(owin)spatstat.data::nbw.seg (psp)spatstat.data::nztrees
(ppp)spatstat.data::osteo (hyperframe)spatstat.data::paracou
(ppp)spatstat.data::ponderosa (ppp)spatstat.data::ponderosa.extra
(list)spatstat.data::pyramidal (hyperframe)spatstat.data::redwood
(ppp)spatstat.data::redwood3 (ppp)spatstat.data::redwoodfull
(ppp)spatstat.data::redwoodfull.extra (list)spatstat.data::residualspaper
(list)spatstat.data::shapley (ppp)spatstat.data::shapley.extra
(list)spatstat.data::simba (hyperframe)spatstat.data::simdat
(ppp)spatstat.data::simplenet (linnet)spatstat.data::spiders
(lpp)spatstat.data::sporophores (ppp)spatstat.data::spruces
(ppp)spatstat.data::stonetools (ppp)spatstat.data::swedishpines
(ppp)spatstat.data::urkiola (ppp)spatstat.data::vesicles
(ppp)spatstat.data::vesicles.extra (solist)spatstat.data::waka
(ppp)spatstat.data::waterstriders (ppplist)RandomFields::ca20.df
(geodata.frame)RandomFields::soil (data.frame)RandomFields::weather (matrix)
csv xlsx json ndjson RData rds

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RandomFieldsRandomFieldsUtilsspatstatspatstat.corespatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.utils

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THE SPATSTAT TEAM



 * r-universe/spatstat
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 * 12 packages
 * 6 articles
 * 61 datasets
 * 14 contributors
 * 14 followers

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 * Adrian Baddeley

 * Martin Schlather

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# Install in R: install.packages('', repos =
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