# spatstat v1.52-1

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## 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 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()). 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.

## Functions in spatstat

Name | Description | |

DiggleGatesStibbard | Diggle-Gates-Stibbard Point Process Model | |

DiggleGratton | Diggle-Gratton model | |

CDF | Cumulative Distribution Function From Kernel Density Estimate | |

Concom | The Connected Component Process Model | |

Extract.fasp | Extract Subset of Function Array | |

Extract.fv | Extract or Replace Subset of Function Values | |

AreaInter | The Area Interaction Point Process Model | |

BadGey | Hybrid Geyer Point Process Model | |

Emark | Diagnostics for random marking | |

Extract.anylist | Extract or Replace Subset of a List of Things | |

Extract.leverage.ppm | Extract Subset of Leverage Object | |

Extract.linim | Extract Subset of Pixel Image on Linear Network | |

Extract.quad | Subset of Quadrature Scheme | |

Extract.solist | Extract or Replace Subset of a List of Spatial Objects | |

Extract.linnet | Extract Subset of Linear Network | |

Extract.listof | Extract or Replace Subset of a List of Things | |

Extract.ppx | Extract Subset of Multidimensional Point Pattern | |

Gdot | Multitype Nearest Neighbour Distance Function (i-to-any) | |

Gest | Nearest Neighbour Distance Function G | |

Extract.influence.ppm | Extract Subset of Influence Object | |

Extract.layered | Extract or Replace Subset of a Layered Object | |

Extract.owin | Extract Subset of Window | |

Extract.ppp | Extract or Replace Subset of Point Pattern | |

Finhom | Inhomogeneous Empty Space Function | |

FmultiInhom | Inhomogeneous Marked F-Function | |

HierHard | The Hierarchical Hard Core Point Process Model | |

HierStrauss | The Hierarchical Strauss Point Process Model | |

K3est | K-function of a Three-Dimensional Point Pattern | |

Kcom | Model Compensator of K Function | |

Kdot | Multitype K Function (i-to-any) | |

Kdot.inhom | Inhomogeneous Multitype K Dot Function | |

Extract.lpp | Extract Subset of Point Pattern on Linear Network | |

Extract.msr | Extract Subset of Signed or Vector Measure | |

Extract.tess | Extract or Replace Subset of Tessellation | |

F3est | Empty Space Function of a Three-Dimensional Point Pattern | |

Gcom | Model Compensator of Nearest Neighbour Function | |

Gcross | Multitype Nearest Neighbour Distance Function (i-to-j) | |

Extract.hyperframe | Extract or Replace Subset of Hyperframe | |

Extract.im | Extract Subset of Image | |

Extract.splitppp | Extract or Replace Sub-Patterns | |

[.ssf | Subset of spatially sampled function | |

Fest | Estimate the Empty Space Function or its Hazard Rate | |

Fiksel | The Fiksel Interaction | |

GmultiInhom | Inhomogeneous Marked G-Function | |

Kmodel.dppm | K-function or Pair Correlation Function of a Determinantal Point Process Model | |

Kmodel.kppm | K Function or Pair Correlation Function of Cluster Model or Cox model | |

Lcross | Multitype L-function (cross-type) | |

Lcross.inhom | Inhomogeneous Cross Type L Function | |

Ginhom | Inhomogeneous Nearest Neighbour Function | |

Gmulti | Marked Nearest Neighbour Distance Function | |

Jdot | Multitype J Function (i-to-any) | |

Jest | Estimate the J-function | |

Kinhom | Inhomogeneous K-function | |

Gres | Residual G Function | |

HierStraussHard | The Hierarchical Strauss Hard Core Point Process Model | |

Hybrid | Hybrid Interaction Point Process Model | |

Kcross | Multitype K Function (Cross-type) | |

Geyer | Geyer's Saturation Point Process Model | |

Gfox | Foxall's Distance Functions | |

Jinhom | Inhomogeneous J-function | |

Jmulti | Marked J Function | |

Kmark | Mark-Weighted K Function | |

Ksector | Sector K-function | |

LambertW | Lambert's W Function | |

Linhom | L-function | |

Kcross.inhom | Inhomogeneous Cross K Function | |

Kres | Residual K Function | |

Kscaled | Locally Scaled K-function | |

Math.imlist | S3 Group Generic methods for List of Images | |

Math.im | S3 Group Generic methods for images | |

Ops.msr | Arithmetic Operations on Measures | |

Ord | Generic Ord Interaction model | |

Saturated | Saturated Pairwise Interaction model | |

Math.linim | S3 Group Generic Methods for Images on a Linear Network | |

OrdThresh | Ord's Interaction model | |

PPversion | Transform a Function into its P-P or Q-Q Version | |

MinkowskiSum | Minkowski Sum of Windows | |

MultiHard | The Multitype Hard Core Point Process Model | |

Smooth.ppp | Spatial smoothing of observations at irregular points | |

Smooth.ssf | Smooth a Spatially Sampled Function | |

Kest | K-function | |

Kest.fft | K-function using FFT | |

Kmulti.inhom | Inhomogeneous Marked K-Function | |

Kovesi | Colour Sequences with Uniform Perceptual Contrast | |

LennardJones | The Lennard-Jones Potential | |

Smooth | Spatial smoothing of data | |

Window | Extract or Change the Window of a Spatial Object | |

WindowOnly | Extract Window of Spatial Object | |

addvar | Added Variable Plot for Point Process Model | |

Extract.psp | Extract Subset of Line Segment Pattern | |

Frame | Extract or Change the Containing Rectangle of a Spatial Object | |

G3est | Nearest Neighbour Distance Distribution Function of a Three-Dimensional Point Pattern | |

Hardcore | The Hard Core Point Process Model | |

Hest | Spherical Contact Distribution Function | |

Smoothfun.ppp | Smooth Interpolation of Marks as a Spatial Function | |

Softcore | The Soft Core Point Process Model | |

affine.tess | Apply Geometrical Transformation To Tessellation | |

allstats | Calculate four standard summary functions of a point pattern. | |

Lest | L-function | |

Pairwise | Generic Pairwise Interaction model | |

Smooth.fv | Apply Smoothing to Function Values | |

Replace.im | Reset Values in Subset of Image | |

SatPiece | Piecewise Constant Saturated Pairwise Interaction Point Process Model | |

adaptive.density | Intensity Estimate of Point Pattern Using Tessellation | |

add.texture | Fill Plot With Texture | |

affine.lpp | Apply Geometrical Transformations to Point Pattern on a Linear Network | |

PairPiece | The Piecewise Constant Pairwise Interaction Point Process Model | |

Smooth.msr | Smooth a Signed or Vector-Valued Measure | |

Strauss | The Strauss Point Process Model | |

StraussHard | The Strauss / Hard Core Point Process Model | |

affine.im | Apply Affine Transformation To Pixel Image | |

anova.ppm | ANOVA for Fitted Point Process Models | |

anova.slrm | Analysis of Deviance for Spatial Logistic Regression Models | |

as.box3 | Convert Data to Three-Dimensional Box | |

as.boxx | Convert Data to Multi-Dimensional Box | |

as.data.frame.tess | Convert Tessellation to Data Frame | |

affine | Apply Affine Transformation | |

alltypes | Calculate Summary Statistic for All Types in a Multitype Point Pattern | |

amacrine | Hughes' Amacrine Cell Data | |

areaGain | Difference of Disc Areas | |

Iest | Estimate the I-function | |

Jcross | Multitype J Function (i-to-j) | |

Kmeasure | Reduced Second Moment Measure | |

Kmodel | K Function or Pair Correlation Function of a Point Process Model | |

Kmodel.ppm | K Function or Pair Correlation Function of Gibbs Point Process model | |

Kmulti | Marked K-Function | |

Ldot | Multitype L-function (i-to-any) | |

Ldot.inhom | Inhomogeneous Multitype L Dot Function | |

affine.linnet | Apply Geometrical Transformations to a Linear Network | |

anemones | Beadlet Anemones Data | |

angles.psp | Orientation Angles of Line Segments | |

applynbd | Apply Function to Every Neighbourhood in a Point Pattern | |

area.owin | Area of a Window | |

areaLoss | Difference of Disc Areas | |

as.function.owin | Convert Window to Indicator Function | |

as.function.tess | Convert a Tessellation to a Function | |

as.fv | Convert Data To Class fv | |

as.function.fv | Convert Function Value Table to Function | |

as.linnet.linim | Extract Linear Network from Data on a Linear Network | |

as.linnet.psp | Convert Line Segment Pattern to Linear Network | |

as.mask.psp | Convert Line Segment Pattern to Binary Pixel Mask | |

as.matrix.im | Convert Pixel Image to Matrix or Array | |

austates | Australian States and Mainland Territories | |

bc.ppm | Bias Correction for Fitted Model | |

berman.test | Berman's Tests for Point Process Model | |

betacells | Beta Ganglion Cells in Cat Retina | |

boxx | Multi-Dimensional Box | |

bramblecanes | Hutchings' Bramble Canes data | |

affine.owin | Apply Affine Transformation To Window | |

anova.lppm | ANOVA for Fitted Point Process Models on Linear Network | |

anova.mppm | ANOVA for Fitted Point Process Models for Replicated Patterns | |

as.data.frame.envelope | Coerce Envelope to Data Frame | |

as.data.frame.im | Convert Pixel Image to Data Frame | |

as.data.frame.owin | Convert Window to Data Frame | |

as.interact | Extract Interaction Structure | |

as.data.frame.hyperframe | Coerce Hyperframe to Data Frame | |

as.function.im | Convert Pixel Image to Function of Coordinates | |

as.function.leverage.ppm | Convert Leverage Object to Function of Coordinates | |

as.hyperframe.ppx | Extract coordinates and marks of multidimensional point pattern | |

as.im | Convert to Pixel Image | |

as.lpp | Convert Data to a Point Pattern on a Linear Network | |

as.mask | Pixel Image Approximation of a Window | |

as.rectangle | Window Frame | |

as.solist | Convert List of Two-Dimensional Spatial Objects | |

as.hyperframe | Convert Data to Hyperframe | |

as.matrix.owin | Convert Pixel Image to Matrix | |

as.owin | Convert Data To Class owin | |

bdist.pixels | Distance to Boundary of Window | |

bdist.points | Distance to Boundary of Window | |

blur | Apply Gaussian Blur to a Pixel Image | |

border | Border Region of a Window | |

boundingcircle | Smallest Enclosing Circle | |

box3 | Three-Dimensional Box | |

bw.ppl | Likelihood Cross Validation Bandwidth Selection for Kernel Density | |

bw.relrisk | Cross Validated Bandwidth Selection for Relative Risk Estimation | |

beachcolours | Create Colour Scheme for a Range of Numbers | |

beginner | Print Introduction For Beginners | |

branchlabelfun | Tree Branch Membership Labelling Function | |

bw.frac | Bandwidth Selection Based on Window Geometry | |

bw.pcf | Cross Validated Bandwidth Selection for Pair Correlation Function | |

cauchy.estpcf | Fit the Neyman-Scott cluster process with Cauchy kernel | |

cbind.hyperframe | Combine Hyperframes by Rows or by Columns | |

bronzefilter | Bronze gradient filter data | |

bw.scott | Scott's Rule for Bandwidth Selection for Kernel Density | |

bw.smoothppp | Cross Validated Bandwidth Selection for Spatial Smoothing | |

clip.infline | Intersect Infinite Straight Lines with a Window | |

clmfires | Castilla-La Mancha Forest Fires | |

clusterfield | Field of clusters | |

clusterfit | Fit Cluster or Cox Point Process Model via Minimum Contrast | |

colourtools | Convert and Compare Colours in Different Formats | |

commonGrid | Determine A Common Spatial Domain And Pixel Resolution | |

contour.im | Contour plot of pixel image | |

contour.imlist | Array of Contour Plots | |

convexhull | Convex Hull | |

as.layered | Convert Data To Layered Object | |

as.ppp | Convert Data To Class ppp | |

as.psp | Convert Data To Class psp | |

cdf.test | Spatial Distribution Test for Point Pattern or Point Process Model | |

cdf.test.mppm | Spatial Distribution Test for Multiple Point Process Model | |

clickdist | Interactively Measure Distance | |

convexhull.xy | Convex Hull of Points | |

crossdist.default | Pairwise distances between two different sets of points | |

crossdist.lpp | Pairwise distances between two point patterns on a linear network | |

dclf.progress | Progress Plot of Test of Spatial Pattern | |

dclf.sigtrace | Significance Trace of Cressie-Loosmore-Ford or Maximum Absolute Deviation Test | |

clickbox | Interactively Define a Rectangle | |

delaunayNetwork | Linear Network of Delaunay Triangulation or Dirichlet Tessellation | |

deletebranch | Delete or Extract a Branch of a Tree | |

dfbetas.ppm | Parameter influence measure | |

dg.envelope | Global Envelopes for Dao-Genton Test | |

dilation | Morphological Dilation | |

dim.detpointprocfamily | Dimension of Determinantal Point Process Model | |

distfun | Distance Map as a Function | |

distfun.lpp | Distance Map on Linear Network | |

MultiStrauss | The Multitype Strauss Point Process Model | |

MultiStraussHard | The Multitype/Hard Core Strauss Point Process Model | |

Penttinen | Penttinen Interaction | |

Poisson | Poisson Point Process Model | |

Triplets | The Triplet Point Process Model | |

Tstat | Third order summary statistic | |

affine.ppp | Apply Affine Transformation To Point Pattern | |

affine.psp | Apply Affine Transformation To Line Segment Pattern | |

ants | Harkness-Isham ants' nests data | |

anyNA.im | Check Whether Image Contains NA Values | |

anylist | List of Objects | |

append.psp | Combine Two Line Segment Patterns | |

clusterkernel | Extract Cluster Offspring Kernel | |

clusterradius | Compute or Extract Effective Range of Cluster Kernel | |

compatible.fasp | Test Whether Function Arrays Are Compatible | |

compatible.fv | Test Whether Function Objects Are Compatible | |

as.tess | Convert Data To Tessellation | |

auc | Area Under ROC Curve | |

bind.fv | Combine Function Value Tables | |

bits.test | Balanced Independent Two-Stage Monte Carlo Test | |

chicago | Chicago Crime Data | |

chop.tess | Subdivide a Window or Tessellation using a Set of Lines | |

clickjoin | Interactively join vertices on a plot | |

clicklpp | Interactively Add Points on a Linear Network | |

compatible.im | Test Whether Pixel Images Are Compatible | |

compileK | Generic Calculation of K Function and Pair Correlation Function | |

covering | Cover Region with Discs | |

crossdist | Pairwise distances | |

crossdist.pp3 | Pairwise distances between two different three-dimensional point patterns | |

closepairs | Close Pairs of Points | |

closepairs.pp3 | Close Pairs of Points in 3 Dimensions | |

coef.ppm | Coefficients of Fitted Point Process Model | |

coef.slrm | Coefficients of Fitted Spatial Logistic Regression Model | |

bounding.box.xy | Convex Hull of Points | |

boundingbox | Bounding Box of a Window, Image, or Point Pattern | |

bw.stoyan | Stoyan's Rule of Thumb for Bandwidth Selection | |

by.im | Apply Function to Image Broken Down by Factor | |

cells | Biological Cells Point Pattern | |

dppBessel | Bessel Type Determinantal Point Process Model | |

dppCauchy | Generalized Cauchy Determinantal Point Process Model | |

dppkernel | Extract Kernel from Determinantal Point Process Model Object | |

dppm | Fit Determinantal Point Process Model | |

connected | Connected components | |

connected.linnet | Connected Components of a Linear Network | |

copyExampleFiles | Copy Data Files for Example | |

corners | Corners of a rectangle | |

crossing.linnet | Crossing Points between Linear Network and Other Lines | |

as.data.frame.ppp | Coerce Point Pattern to a Data Frame | |

as.data.frame.psp | Coerce Line Segment Pattern to a Data Frame | |

as.linfun | Convert Data to a Function on a Linear Network | |

as.linim | Convert to Pixel Image on Linear Network | |

as.polygonal | Convert a Window to a Polygonal Window | |

as.ppm | Extract Fitted Point Process Model | |

bdist.tiles | Distance to Boundary of Window | |

centroid.owin | Centroid of a window | |

clarkevans | Clark and Evans Aggregation Index | |

clarkevans.test | Clark and Evans Test | |

closetriples | Close Triples of Points | |

bdspots | Breakdown Spots in Microelectronic Materials | |

begins | Check Start of Character String | |

bei | Tropical rain forest trees | |

bugfixes | List Recent Bug Fixes | |

edges | Extract Boundary Edges of a Window. | |

edges2triangles | List Triangles in a Graph | |

emend | Force Model to be Valid | |

emend.ppm | Force Point Process Model to be Valid | |

closing | Morphological Closing | |

collapse.fv | Collapse Several Function Tables into One | |

colourmap | Colour Lookup Tables | |

complement.owin | Take Complement of a Window | |

concatxy | Concatenate x,y Coordinate Vectors | |

exactMPLEstrauss | Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process | |

expand.owin | Apply Expansion Rule | |

fitted.ppm | Fitted Conditional Intensity for Point Process Model | |

fitted.slrm | Fitted Probabilities for Spatial Logistic Regression | |

flu | Influenza Virus Proteins | |

foo | Foo is Not a Real Name | |

hamster | Aherne's hamster tumour data | |

convexify | Weil's Convexifying Operation | |

convolve.im | Convolution of Pixel Images | |

cut.im | Convert Pixel Image from Numeric to Factor | |

cut.lpp | Classify Points in a Point Pattern on a Network | |

crossdist.ppp | Pairwise distances between two different point patterns | |

default.expand | Default Expansion Rule for Simulation of Model | |

default.rmhcontrol | Set Default Control Parameters for Metropolis-Hastings Algorithm. | |

crossing.psp | Crossing Points of Two Line Segment Patterns | |

cut.ppp | Classify Points in a Point Pattern | |

data.ppm | Extract Original Data from a Fitted Point Process Model | |

dendrite | Dendritic Spines Data | |

dg.progress | Progress Plot of Dao-Genton Test of Spatial Pattern | |

dg.sigtrace | Significance Trace of Dao-Genton Test | |

harmonic | Basis for Harmonic Functions | |

hextess | Hexagonal Grid or Tessellation | |

hierpair.family | Hierarchical Pairwise Interaction Process Family | |

demopat | Artificial Data Point Pattern | |

bw.diggle | Cross Validated Bandwidth Selection for Kernel Density | |

by.ppp | Apply a Function to a Point Pattern Broken Down by Factor | |

cauchy.estK | Fit the Neyman-Scott cluster process with Cauchy kernel | |

chorley | Chorley-Ribble Cancer Data | |

hybrid.family | Hybrid Interaction Family | |

hyperframe | Hyper Data Frame | |

influence.ppm | Influence Measure for Spatial Point Process Model | |

inforder.family | Infinite Order Interaction Family | |

intensity | Intensity of a Dataset or a Model | |

intensity.dppm | Intensity of Determinantal Point Process Model | |

intersect.tess | Intersection of Two Tessellations | |

invoke.symbolmap | Plot Data Using Graphics Symbol Map | |

is.empty | Test Whether An Object Is Empty | |

is.hybrid | Test Whether Object is a Hybrid | |

is.stationary | Recognise Stationary and Poisson Point Process Models | |

delaunay | Delaunay Triangulation of Point Pattern | |

delaunayDistance | Distance on Delaunay Triangulation | |

density.psp | Kernel Smoothing of Line Segment Pattern | |

density.splitppp | Kernel Smoothed Intensity of Split Point Pattern | |

deriv.fv | Calculate Derivative of Function Values | |

detpointprocfamilyfun | Construct a New Determinantal Point Process Model Family Function | |

diameter.boxx | Geometrical Calculations for Multi-Dimensional Box | |

dimhat | Estimate Dimension of Central Subspace | |

dirichlet | Dirichlet Tessellation of Point Pattern | |

discs | Union of Discs | |

distcdf | Distribution Function of Interpoint Distance | |

circdensity | Density Estimation for Circular Data | |

clickpoly | Interactively Define a Polygon | |

clickppp | Interactively Add Points | |

diameter.linnet | Diameter and Bounding Radius of a Linear Network | |

distmap | Distance Map | |

distmap.owin | Distance Map of Window | |

dppGauss | Gaussian Determinantal Point Process Model | |

density.lpp | Kernel Estimate of Intensity on a Linear Network | |

density.ppp | Kernel Smoothed Intensity of Point Pattern | |

diameter | Diameter of an Object | |

dppMatern | Whittle-Matern Determinantal Point Process Model | |

dppapproxpcf | Approximate Pair Correlation Function of Determinantal Point Process Model | |

dppeigen | Internal function calculating eig and index | |

distmap.ppp | Distance Map of Point Pattern | |

distmap.psp | Distance Map of Line Segment Pattern | |

dppspecdenrange | Range of Spectral Density of a Determinantal Point Process Model | |

dummify | Convert Data to Numeric Values by Constructing Dummy Variables | |

edge.Ripley | Ripley's Isotropic Edge Correction | |

edge.Trans | Translation Edge Correction | |

effectfun | Compute Fitted Effect of a Spatial Covariate in a Point Process Model | |

ellipse | Elliptical Window. | |

eval.fv | Evaluate Expression Involving Functions | |

eval.im | Evaluate Expression Involving Pixel Images | |

clusterset | Allard-Fraley Estimator of Cluster Feature | |

coef.mppm | Coefficients of Point Process Model Fitted to Multiple Point Patterns | |

compareFit | Residual Diagnostics for Multiple Fitted Models | |

compatible | Test Whether Objects Are Compatible | |

dummy.ppm | Extract Dummy Points Used to Fit a Point Process Model | |

duplicated.ppp | Determine Duplicated Points in a Spatial Point Pattern | |

endpoints.psp | Endpoints of Line Segment Pattern | |

diameter.box3 | Geometrical Calculations for Three-Dimensional Box | |

diameter.owin | Diameter of a Window | |

dilated.areas | Areas of Morphological Dilations | |

discpartarea | Area of Part of Disc | |

connected.lpp | Connected Components of a Point Pattern on a Linear Network | |

connected.ppp | Connected Components of a Point Pattern | |

coords | Extract or Change Coordinates of a Spatial or Spatiotemporal Point Pattern | |

copper | Berman-Huntington points and lines data | |

measureVariation | Positive and Negative Parts, and Variation, of a Measure | |

is.subset.owin | Determine Whether One Window is Contained In Another | |

lgcp.estK | Fit a Log-Gaussian Cox Point Process by Minimum Contrast | |

kaplan.meier | Kaplan-Meier Estimator using Histogram Data | |

crossdist.ppx | Pairwise Distances Between Two Different Multi-Dimensional Point Patterns | |

crossdist.psp | Pairwise distances between two different line segment patterns | |

dclf.test | Diggle-Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests | |

fitted.lppm | Fitted Intensity for Point Process on Linear Network | |

fitted.mppm | Fitted Conditional Intensity for Multiple Point Process Model | |

fv.object | Function Value Table | |

fvnames | Abbreviations for Groups of Columns in Function Value Table | |

linearK | Linear K Function | |

kernel.factor | Scale factor for density kernel | |

nndist | Nearest neighbour distances | |

envelope | Simulation Envelopes of Summary Function | |

eroded.areas | Areas of Morphological Erosions | |

erosion | Morphological Erosion by a Disc | |

finpines | Pine saplings in Finland. | |

fitin.ppm | Extract the Interaction from a Fitted Point Process Model | |

fourierbasis | Fourier Basis Functions | |

fryplot | Fry Plot of Point Pattern | |

ganglia | Beta Ganglion Cells in Cat Retina, Old Version | |

gauss.hermite | Gauss-Hermite Quadrature Approximation to Expectation for Normal Distribution | |

headtail | First or Last Part of a Spatial Pattern | |

heather | Diggle's Heather Data | |

default.dummy | Generate a Default Pattern of Dummy Points | |

demohyper | Demonstration Example of Hyperframe of Spatial Data | |

dg.test | Dao-Genton Adjusted Goodness-Of-Fit Test | |

hyytiala | Scots pines and other trees at Hyytiala | |

identify.ppp | Identify Points in a Point Pattern | |

im.object | Class of Images | |

imcov | Spatial Covariance of a Pixel Image | |

deltametric | Delta Metric | |

diagnose.ppm | Diagnostic Plots for Fitted Point Process Model | |

dirichletAreas | Compute Areas of Tiles in Dirichlet Tessellation | |

dirichletVertices | Vertices and Edges of Dirichlet Tessellation | |

dirichletWeights | Compute Quadrature Weights Based on Dirichlet Tessellation | |

disc | Circular Window | |

intensity.lpp | Empirical Intensity of Point Pattern on Linear Network | |

intensity.ppm | Intensity of Fitted Point Process Model | |

iplot | Point and Click Interface for Displaying Spatial Data | |

ippm | Fit Point Process Model Involving Irregular Trend Parameters | |

lgcp.estpcf | Fit a Log-Gaussian Cox Point Process by Minimum Contrast | |

markconnect | Mark Connection Function | |

linearKcross | Multitype K Function (Cross-type) for Linear Point Pattern | |

gordon | People in Gordon Square | |

gorillas | Gorilla Nesting Sites | |

harmonise.owin | Make Windows Compatible | |

has.close | Check Whether Points Have Close Neighbours | |

discretise | Safely Convert Point Pattern Window to Binary Mask | |

dmixpois | Mixed Poisson Distribution | |

domain | Extract the Domain of any Spatial Object | |

methods.pp3 | Methods for three-dimensional point patterns | |

linequad | Quadrature Scheme on a Linear Network | |

linfun | Function on a Linear Network | |

lintess | Tessellation on a Linear Network | |

model.frame.ppm | Extract the Variables in a Point Process Model | |

is.im | Test Whether An Object Is A Pixel Image | |

is.lpp | Test Whether An Object Is A Point Pattern on a Linear Network | |

is.multitype.ppm | Test Whether A Point Process Model is Multitype | |

is.multitype.ppp | Test Whether A Point Pattern is Multitype | |

identify.psp | Identify Segments in a Line Segment Pattern | |

idw | Inverse-distance weighted smoothing of observations at irregular points | |

im | Create a Pixel Image Object | |

im.apply | Apply Function Pixelwise to List of Images | |

insertVertices | Insert New Vertices in a Linear Network | |

dppparbounds | Parameter Bound for a Determinantal Point Process Model | |

dppspecden | Extract Spectral Density from Determinantal Point Process Model Object | |

edges2vees | List Dihedral Triples in a Graph | |

edit.hyperframe | Invoke Text Editor on Hyperframe | |

lengths.psp | Lengths of Line Segments | |

istat | Point and Click Interface for Exploratory Analysis of Point Pattern | |

marks | Marks of a Point Pattern | |

inside.boxx | Test Whether Points Are Inside A Multidimensional Box | |

intensity.ppp | Empirical Intensity of Point Pattern | |

intensity.ppx | Intensity of a Multidimensional Space-Time Point Pattern | |

is.convex | Test Whether a Window is Convex | |

japanesepines | Japanese Pines Point Pattern | |

linearmarkequal | Mark Connection Function for Multitype Point Pattern on Linear Network | |

letterR | Window in Shape of Letter R | |

linearpcf | Linear Pair Correlation Function | |

linearmarkconnect | Mark Connection Function for Multitype Point Pattern on Linear Network | |

is.dppm | Recognise Fitted Determinantal Point Process Models | |

is.marked.ppp | Test Whether A Point Pattern is Marked | |

km.rs | Kaplan-Meier and Reduced Sample Estimator using Histograms | |

is.multitype | Test whether Object is Multitype | |

lixellate | Subdivide Segments of a Network | |

maxnndist | Compute Minimum or Maximum Nearest-Neighbour Distance | |

markcorr | Mark Correlation Function | |

matrixpower | Power of a Matrix | |

parres | Partial Residuals for Point Process Model | |

model.matrix.ppm | Extract Design Matrix from Point Process Model | |

mergeLevels | Merge Levels of a Factor | |

methods.objsurf | Methods for Objective Function Surfaces | |

divide.linnet | Divide Linear Network at Cut Points | |

dkernel | Kernel distributions and random generation | |

dppPowerExp | Power Exponential Spectral Determinantal Point Process Model | |

dppapproxkernel | Approximate Determinantal Point Process Kernel | |

envelope.pp3 | Simulation Envelopes of Summary Function for 3D Point Pattern | |

envelopeArray | Array of Simulation Envelopes of Summary Function | |

erosionAny | Morphological Erosion of Windows | |

eval.fasp | Evaluate Expression Involving Function Arrays | |

matclust.estK | Fit the Matern Cluster Point Process by Minimum Contrast | |

logLik.kppm | Log Likelihood and AIC for Fitted Cox or Cluster Point Process Model | |

linearpcfcross | Multitype Pair Correlation Function (Cross-type) for Linear Point Pattern | |

methods.ppx | Methods for Multidimensional Space-Time Point Patterns | |

model.images | Compute Images of Constructed Covariates | |

model.depends | Identify Covariates Involved in each Model Term | |

nsegments | Number of Line Segments in a Line Segment Pattern | |

nndensity.ppp | Estimate Intensity of Point Pattern Using Nearest Neighbour Distances | |

plot.textstring | Plot a Text String | |

nvertices | Count Number of Vertices | |

quantile.density | Quantiles of a Density Estimate | |

nnorient | Nearest Neighbour Orientation Distribution | |

nnwhich | Nearest neighbour | |

owin.object | Class owin | |

owin | Create a Window | |

plot.anylist | Plot a List of Things | |

pairs.linim | Scatterplot Matrix for Pixel Images on a Linear Network | |

pairsat.family | Saturated Pairwise Interaction Point Process Family | |

pcf | Pair Correlation Function | |

pcfinhom | Inhomogeneous Pair Correlation Function | |

pcfmulti | Marked pair correlation function | |

edit.ppp | Invoke Text Editor on Spatial Data | |

eem | Exponential Energy Marks | |

envelope.envelope | Recompute Envelopes | |

markcrosscorr | Mark Cross-Correlation Function | |

logLik.mppm | Log Likelihood and AIC for Multiple Point Process Model | |

matclust.estpcf | Fit the Matern Cluster Point Process by Minimum Contrast Using Pair Correlation | |

fixef.mppm | Extract Fixed Effects from Point Process Model | |

flipxy | Exchange X and Y Coordinates | |

formula.fv | Extract or Change the Plot Formula for a Function Value Table | |

envelope.lpp | Envelope for Point Patterns on Linear Network | |

plot.bermantest | Plot Result of Berman Test | |

plot.hyperframe | Plot Entries in a Hyperframe | |

plot.im | Plot a Pixel Image | |

print.im | Print Brief Details of an Image | |

eval.linim | Evaluate Expression Involving Pixel Images on Linear Network | |

ewcdf | Weighted Empirical Cumulative Distribution Function | |

fardist | Farthest Distance to Boundary of Window | |

fasp.object | Function Arrays for Spatial Patterns | |

funxy | Spatial Function Class | |

formula.ppm | Model Formulae for Gibbs Point Process Models | |

grow.boxx | Add margins to box in any dimension | |

grow.rectangle | Add margins to rectangle | |

harmonise.im | Make Pixel Images Compatible | |

harmonise.msr | Make Measures Compatible | |

hist.funxy | Histogram of Values of a Spatial Function | |

hist.im | Histogram of Pixel Values in an Image | |

increment.fv | Increments of a Function | |

infline | Infinite Straight Lines | |

inside.owin | Test Whether Points Are Inside A Window | |

plot.mppm | plot a Fitted Multiple Point Process Model | |

plot.texturemap | Plot a Texture Map | |

ppm.object | Class of Fitted Point Process Models | |

plot.msr | Plot a Signed or Vector-Valued Measure | |

plot.plotppm | Plot a plotppm Object Created by plot.ppm | |

multiplicity.ppp | Count Multiplicity of Duplicate Points | |

methods.box3 | Methods for Three-Dimensional Box | |

nobjects | Count Number of Geometrical Objects in a Spatial Dataset | |

methods.rho2hat | Methods for Intensity Functions of Two Spatial Covariates | |

plot.pp3 | Plot a Three-Dimensional Point Pattern | |

pool.envelope | Pool Data from Several Envelopes | |

print.owin | Print Brief Details of a Spatial Window | |

integral.im | Integral of a Pixel Image | |

interp.im | Interpolate a Pixel Image | |

intersect.owin | Intersection, Union or Set Subtraction of Windows | |

is.marked | Test Whether Marks Are Present | |

is.marked.ppm | Test Whether A Point Process Model is Marked | |

is.ppp | Test Whether An Object Is A Point Pattern | |

methods.kppm | Methods for Cluster Point Process Models | |

is.rectangle | Determine Type of Window | |

lansing | Lansing Woods Point Pattern | |

pool.anylist | Pool Data from a List of Objects | |

psstA | Pseudoscore Diagnostic For Fitted Model against Area-Interaction Alternative | |

print.psp | Print Brief Details of a Line Segment Pattern Dataset | |

ppm.ppp | Fit Point Process Model to Point Pattern Data | |

print.quad | Print a Quadrature Scheme | |

rSSI | Simulate Simple Sequential Inhibition | |

quantile.ewcdf | Quantiles of Weighted Empirical Cumulative Distribution Function | |

quantess | Quantile Tessellation | |

psstG | Pseudoscore Diagnostic For Fitted Model against Saturation Alternative | |

quantile.im | Sample Quantiles of Pixel Image | |

rotate.infline | Rotate or Shift Infinite Lines | |

rStraussHard | Perfect Simulation of the Strauss-Hardcore Process | |

rescale.ppp | Convert Point Pattern to Another Unit of Length | |

rThomas | Simulate Thomas Process | |

rStrauss | Perfect Simulation of the Strauss Process | |

rescale.owin | Convert Window to Another Unit of Length | |

reduced.sample | Reduced Sample Estimator using Histogram Data | |

methods.boxx | Methods for Multi-Dimensional Box | |

methods.units | Methods for Units | |

methods.ssf | Methods for Spatially Sampled Functions | |

reach.dppm | Range of Interaction for a Determinantal Point Process Model | |

spatstat-internal | Internal spatstat functions | |

rex | Richardson Extrapolation | |

rmpoint | Generate N Random Multitype Points | |

latest.news | Print News About Latest Version of Package | |

kppm | Fit Cluster or Cox Point Process Model | |

linearpcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Dot-type) for Linear Point Pattern | |

fv | Create a Function Value Table | |

gridcentres | Rectangular grid of points | |

gridweights | Compute Quadrature Weights Based on Grid Counts | |

residualspaper | Data and Code From JRSS Discussion Paper on Residuals | |

rhohat | Smoothing Estimate of Intensity as Function of a Covariate | |

ripras | Estimate window from points alone | |

pairdist.lpp | Pairwise shortest-path distances between points on a linear network | |

laslett | Laslett's Transform | |

levelset | Level Set of a Pixel Image | |

linearKdot.inhom | Inhomogeneous multitype K Function (Dot-type) for Linear Point Pattern | |

leverage.ppm | Leverage Measure for Spatial Point Process Model | |

rshift | Random Shift | |

rmhstart | Determine Initial State for Metropolis-Hastings Simulation. | |

rotate.owin | Rotate a Window | |

rpoislpp | Poisson Point Process on a Linear Network | |

markvario | Mark Variogram | |

layered | Create List of Plotting Layers | |

lpp | Create Point Pattern on Linear Network | |

lineardirichlet | Dirichlet Tessellation on a Linear Network | |

logLik.slrm | Loglikelihood of Spatial Logistic Regression | |

lineardisc | Compute Disc of Given Radius in Linear Network | |

midpoints.psp | Midpoints of Line Segment Pattern | |

logLik.ppm | Log Likelihood and AIC for Point Process Model | |

linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |

harmonise | Make Objects Compatible | |

harmonise.fv | Make Function Tables Compatible | |

linearpcfdot | Multitype Pair Correlation Function (Dot-type) for Linear Point Pattern | |

linearKinhom | Inhomogeneous Linear K Function | |

linearpcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-type) for Linear Point Pattern | |

methods.funxy | Methods for Spatial Functions | |

humberside | Humberside Data on Childhood Leukaemia and Lymphoma | |

improve.kppm | Improve Intensity Estimate of Fitted Cluster Point Process Model | |

incircle | Find Largest Circle Inside Window | |

integral.linim | Integral on a Linear Network | |

integral.msr | Integral of a Measure | |

hopskel | Hopkins-Skellam Test | |

markstat | Summarise Marks in Every Neighbourhood in a Point Pattern | |

localK | Neighbourhood density function | |

lurking | Lurking variable plot | |

localKinhom | Inhomogeneous Neighbourhood Density Function | |

nnwhich.ppx | Nearest Neighbours in Any Dimensions | |

nnmark | Mark of Nearest Neighbour | |

mucosa | Cells in Gastric Mucosa | |

nncross | Nearest Neighbours Between Two Patterns | |

methods.dppm | Methods for Determinantal Point Process Models | |

matchingdist | Distance for a Point Pattern Matching | |

methods.zclustermodel | Methods for Cluster Models | |

lppm | Fit Point Process Model to Point Pattern on Linear Network | |

nnfun | Nearest Neighbour Index Map as a Function | |

nncorr | Nearest-Neighbour Correlation Indices of Marked Point Pattern | |

nnmap | K-th Nearest Point Map | |

perspPoints | Draw Points or Lines on a Surface Viewed in Perspective | |

opening | Morphological Opening | |

parameters | Extract Model Parameters in Understandable Form | |

paracou | Kimboto trees at Paracou, French Guiana | |

pixellate.owin | Convert Window to Pixel Image | |

intensity.quadratcount | Intensity Estimates Using Quadrat Counts | |

interp.colourmap | Interpolate smoothly between specified colours | |

is.connected | Determine Whether an Object is Connected | |

is.connected.ppp | Determine Whether a Point Pattern is Connected | |

is.owin | Test Whether An Object Is A Window | |

pairdist.pp3 | Pairwise distances in Three Dimensions | |

ord.family | Ord Interaction Process Family | |

ponderosa | Ponderosa Pine Tree Point Pattern | |

pairdist.ppp | Pairwise distances | |

methods.fii | Methods for Fitted Interactions | |

nearestsegment | Find Line Segment Nearest to Each Point | |

objsurf | Objective Function Surface | |

methods.lpp | Methods for Point Patterns on a Linear Network | |

methods.lppm | Methods for Fitted Point Process Models on a Linear Network | |

nearest.raster.point | Find Pixel Nearest to a Given Point | |

overlap.owin | Compute Area of Overlap | |

model.matrix.slrm | Extract Design Matrix from Spatial Logistic Regression Model | |

moribund | Outdated Functions | |

nnfun.lpp | Nearest Neighbour Map on Linear Network | |

pairorient | Point Pair Orientation Distribution | |

is.ppm | Test Whether An Object Is A Fitted Point Process Model | |

kernel.moment | Moment of Smoothing Kernel | |

linearKcross.inhom | Inhomogeneous multitype K Function (Cross-type) for Linear Point Pattern | |

kernel.squint | Integral of Squared Kernel | |

linim | Create Pixel Image on Linear Network | |

layerplotargs | Extract or Replace the Plot Arguments of a Layered Object | |

layout.boxes | Generate a Row or Column Arrangement of Rectangles. | |

marks.psp | Marks of a Line Segment Pattern | |

linearKdot | Multitype K Function (Dot-type) for Linear Point Pattern | |

methods.rhohat | Methods for Intensity Functions of Spatial Covariate | |

localpcf | Local pair correlation function | |

linnet | Create a Linear Network | |

osteo | Osteocyte Lacunae Data: Replicated Three-Dimensional Point Patterns | |

nztrees | New Zealand Trees Point Pattern | |

pool.fasp | Pool Data from Several Function Arrays | |

pixelcentres | Extract Pixel Centres as Point Pattern | |

plot.symbolmap | Plot a Graphics Symbol Map | |

pcf.fv | Pair Correlation Function obtained from K Function | |

pairs.im | Scatterplot Matrix for Pixel Images | |

rshift.ppp | Randomly Shift a Point Pattern | |

sidelengths.owin | Side Lengths of Enclosing Rectangle of a Window | |

summary.owin | Summary of a Spatial Window | |

rpoispp | Generate Poisson Point Pattern | |

plot.cdftest | Plot a Spatial Distribution Test | |

pcf.fasp | Pair Correlation Function obtained from array of K functions | |

pool.fv | Pool Several Functions | |

plot.imlist | Plot a List of Images | |

pixellate.ppp | Convert Point Pattern to Pixel Image | |

persp.im | Perspective Plot of Pixel Image | |

plot.lintess | Plot a Tessellation on a Linear Network | |

runifpoint3 | Generate N Uniform Random Points in Three Dimensions | |

runifpointOnLines | Generate N Uniform Random Points On Line Segments | |

sessionLibs | Print Names and Version Numbers of Libraries Loaded | |

simba | Simulated data from a two-group experiment with replication within each group. | |

plot.slrm | Plot a Fitted Spatial Logistic Regression | |

plot.influence.ppm | Plot Influence Measure | |

plot.solist | Plot a List of Spatial Objects | |

plot.listof | Plot a List of Things | |

mean.im | Mean and Median of Pixel Values in an Image | |

mppm | Fit Point Process Model to Several Point Patterns | |

logLik.dppm | Log Likelihood and AIC for Fitted Determinantal Point Process Model | |

selfcut.psp | Cut Line Segments Where They Intersect | |

simulate.dppm | Simulation of Determinantal Point Process Model | |

spatstat-deprecated | Deprecated spatstat functions | |

spokes | Spokes pattern of dummy points | |

simulate.kppm | Simulate a Fitted Cluster Point Process Model | |

pixellate | Convert Spatial Object to Pixel Image | |

plot.layered | Layered Plot | |

pppmatching | Create a Point Matching | |

plot.colourmap | Plot a Colour Map | |

subfits | Extract List of Individual Point Process Models | |

symbolmap | Graphics Symbol Map | |

sporophores | Sporophores Data | |

studpermu.test | Studentised Permutation Test | |

marktable | Tabulate Marks in Neighbourhood of Every Point in a Point Pattern | |

lut | Lookup Tables | |

mincontrast | Method of Minimum Contrast | |

methods.linim | Methods for Images on a Linear Network | |

treeprune | Prune Tree to Given Level | |

plot.quadratcount | Plot Quadrat Counts | |

ppp | Create a Point Pattern | |

quadrat.test.mppm | Chi-Squared Test for Multiple Point Process Model Based on Quadrat Counts | |

methods.linnet | Methods for Linear Networks | |

nbfires | Point Patterns of New Brunswick Forest Fires | |

murchison | Murchison gold deposits | |

nndist.ppx | Nearest Neighbour Distances in Any Dimensions | |

miplot | Morisita Index Plot | |

plot.quadrattest | Display the result of a quadrat counting test. | |

prune.rppm | Prune a Recursively Partitioned Point Process Model | |

quadrat.test | Dispersion Test for Spatial Point Pattern Based on Quadrat Counts | |

rDGS | Perfect Simulation of the Diggle-Gates-Stibbard Process | |

npoints | Number of Points in a Point Pattern | |

nndist.psp | Nearest neighbour distances between line segments | |

profilepl | Fit Models by Profile Maximum Pseudolikelihood or AIC | |

nncross.lpp | Nearest Neighbours on a Linear Network | |

nncross.pp3 | Nearest Neighbours Between Two Patterns in 3D | |

plot.lpp | Plot Point Pattern on Linear Network | |

plot.leverage.ppm | Plot Leverage Function | |

plot.lppm | Plot a Fitted Point Process Model on a Linear Network | |

plot.ppp | plot a Spatial Point Pattern | |

plot.ppm | plot a Fitted Point Process Model | |

lohboot | Bootstrap Confidence Bands for Summary Function | |

methods.slrm | Methods for Spatial Logistic Regression Models | |

longleaf | Longleaf Pines Point Pattern | |

marks.tess | Marks of a Tessellation | |

methods.layered | Methods for Layered Objects | |

msr | Signed or Vector-Valued Measure | |

mean.linim | Mean, Median, Quantiles of Pixel Values on a Linear Network | |

methods.linfun | Methods for Functions on Linear Network | |

summary.ppm | Summarizing a Fitted Point Process Model | |

vargamma.estpcf | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel | |

will.expand | Test Expansion Rule | |

swedishpines | Swedish Pines Point Pattern | |

npfun | Dummy Function Returns Number of Points | |

pcf.ppp | Pair Correlation Function of Point Pattern | |

psib | Sibling Probability of Cluster Point Process | |

pairdist.default | Pairwise distances | |

pairdist.psp | Pairwise distances between line segments | |

nnwhich.lpp | Identify Nearest Neighbours on a Linear Network | |

nndist.pp3 | Nearest neighbour distances in three dimensions | |

nnclean | Nearest Neighbour Clutter Removal | |

nnwhich.pp3 | Nearest neighbours in three dimensions | |

periodify | Make Periodic Copies of a Spatial Pattern | |

nestsplit | Nested Split | |

pcfcross | Multitype pair correlation function (cross-type) | |

plot.tess | Plot a tessellation | |

project2segment | Move Point To Nearest Line | |

pyramidal | Pyramidal Neurons in Cingulate Cortex | |

predict.mppm | Prediction for Fitted Multiple Point Process Model | |

pppmatching.object | Class of Point Matchings | |

rotate.im | Rotate a Pixel Image | |

predict.ppm | Prediction from a Fitted Point Process Model | |

project2set | Find Nearest Point in a Region | |

unique.ppp | Extract Unique Points from a Spatial Point Pattern | |

unitname | Name for Unit of Length | |

triangulate.owin | Decompose Window into Triangles | |

rLGCP | Simulate Log-Gaussian Cox Process | |

quadscheme | Generate a Quadrature Scheme from a Point Pattern | |

rMatClust | Simulate Matern Cluster Process | |

qqplot.ppm | Q-Q Plot of Residuals from Fitted Point Process Model | |

pool | Pool Data | |

ppmInfluence | Leverage and Influence Measures for Spatial Point Process Model | |

predict.slrm | Predicted or Fitted Values from Spatial Logistic Regression | |

predict.rppm | Make Predictions From a Recursively Partitioned Point Process Model | |

nndist.lpp | Nearest neighbour distances on a linear network | |

pairdist | Pairwise distances | |

plot.linim | Plot Pixel Image on Linear Network | |

valid | Check Whether Point Process Model is Valid | |

vargamma.estK | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel | |

urkiola | Urkiola Woods Point Pattern | |

whist | Weighted Histogram | |

with.msr | Evaluate Expression Involving Components of a Measure | |

with.ssf | Evaluate Expression in a Spatially Sampled Function | |

pseudoR2 | Calculate Pseudo-R-Squared for Point Process Model | |

psp.object | Class of Line Segment Patterns | |

rose | Rose Diagram | |

psst | Pseudoscore Diagnostic For Fitted Model against General Alternative | |

rescue.rectangle | Convert Window Back To Rectangle | |

quadscheme.logi | Generate a Logistic Regression Quadrature Scheme from a Point Pattern | |

range.fv | Range of Function Values | |

rNeymanScott | Simulate Neyman-Scott Process | |

sharpen | Data Sharpening of Point Pattern | |

rcellnumber | Generate Random Numbers of Points for Cell Process | |

rMaternI | Simulate Matern Model I | |

rDiggleGratton | Perfect Simulation of the Diggle-Gratton Process | |

rMaternII | Simulate Matern Model II | |

rescale | Convert dataset to another unit of length | |

panel.contour | Panel Plots using Colour Image or Contour Lines | |

padimage | Pad the Border of a Pixel Image | |

pairwise.family | Pairwise Interaction Process Family | |

perimeter | Perimeter Length of Window | |

plot.dppm | Plot a fitted determinantal point process | |

pcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-Type) | |

predict.lppm | Predict Point Process Model on Linear Network | |

rags | Alternating Gibbs Sampler for Multitype Point Processes | |

pairdist.ppx | Pairwise Distances in Any Dimensions | |

pcf3est | Pair Correlation Function of a Three-Dimensional Point Pattern | |

pcfdot | Multitype pair correlation function (i-to-any) | |

rVarGamma | Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel | |

rcell | Simulate Baddeley-Silverman Cell Process | |

residuals.mppm | Residuals for Point Process Model Fitted to Multiple Point Patterns | |

plot.envelope | Plot a Simulation Envelope | |

plot.psp | plot a Spatial Line Segment Pattern | |

plot.fasp | Plot a Function Array | |

plot.fv | Plot Function Values | |

pixellate.psp | Convert Line Segment Pattern to Pixel Image | |

pcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Type-i-To-Any-Type) | |

plot.scan.test | Plot Result of Scan Test | |

pixelquad | Quadrature Scheme Based on Pixel Grid | |

plot.laslett | Plot Laslett Transform | |

simdat | Simulated Point Pattern | |

residuals.ppm | Residuals for Fitted Point Process Model | |

rescale.im | Convert Pixel Image to Another Unit of Length | |

rmhmodel | Define Point Process Model for Metropolis-Hastings Simulation. | |

rjitter | Random Perturbation of a Point Pattern | |

roc | Receiver Operating Characteristic | |

rknn | Theoretical Distribution of Nearest Neighbour Distance | |

rounding | Detect Numerical Rounding | |

rmhmodel.default | Build Point Process Model for Metropolis-Hastings Simulation. | |

rshift.psp | Randomly Shift a Line Segment Pattern | |

rpoint | Generate N Random Points | |

rshift.splitppp | Randomly Shift a List of Point Patterns | |

rPenttinen | Perfect Simulation of the Penttinen Process | |

reflect | Reflect In Origin | |

rescale.psp | Convert Line Segment Pattern to Another Unit of Length | |

plot.yardstick | Plot a Yardstick or Scale Bar | |

plot.linnet | Plot a linear network | |

pppdist | Distance Between Two Point Patterns | |

pointsOnLines | Place Points Evenly Along Specified Lines | |

runifpointx | Generate N Uniform Random Points in Any Dimensions | |

spatialcdf | Spatial Cumulative Distribution Function | |

scalardilate | Apply Scalar Dilation | |

ranef.mppm | Extract Random Effects from Point Process Model | |

relrisk.ppm | Parametric Estimate of Spatially-Varying Relative Risk | |

regularpolygon | Create A Regular Polygon | |

rmh.default | Simulate Point Process Models using the Metropolis-Hastings Algorithm. | |

relrisk | Estimate of Spatially-Varying Relative Risk | |

rotate | Rotate | |

rmh.ppm | Simulate from a Fitted Point Process Model | |

plot.kppm | Plot a fitted cluster point process | |

pool.quadrattest | Pool Several Quadrat Tests | |

plot.owin | Plot a Spatial Window | |

plot.onearrow | Plot an Arrow | |

plot.ssf | Plot a Spatially Sampled Function | |

plot.rppm | Plot a Recursively Partitioned Point Process Model | |

plot.splitppp | Plot a List of Point Patterns | |

points.lpp | Draw Points on Existing Plot | |

plot.quad | Plot a Spatial Quadrature Scheme | |

polynom | Polynomial in One or Two Variables | |

ppp.object | Class of Point Patterns | |

predict.kppm | Prediction from a Fitted Cluster Point Process Model | |

quad.ppm | Extract Quadrature Scheme Used to Fit a Point Process Model | |

scanpp | Read Point Pattern From Data File | |

print.ppm | Print a Fitted Point Process Model | |

simplenet | Simple Example of Linear Network | |

simulate.lppm | Simulate a Fitted Point Process Model on a Linear Network | |

shift | Apply Vector Translation | |

spatdim | Spatial Dimension of a Dataset | |

simulate.mppm | Simulate a Point Process Model Fitted to Several Point Patterns | |

suffstat | Sufficient Statistic of Point Process Model | |

stienen | Stienen Diagram | |

stratrand | Stratified random point pattern | |

thomas.estpcf | Fit the Thomas Point Process by Minimum Contrast | |

pp3 | Three Dimensional Point Pattern | |

pool.rat | Pool Data from Several Ratio Objects | |

ppm | Fit Point Process Model to Data | |

quadratresample | Resample a Point Pattern by Resampling Quadrats | |

ppx | Multidimensional Space-Time Point Pattern | |

rMosaicField | Mosaic Random Field | |

predict.dppm | Prediction from a Fitted Determinantal Point Process Model | |

residuals.kppm | Residuals for Fitted Cox or Cluster Point Process Model | |

progressreport | Print Progress Reports | |

thomas.estK | Fit the Thomas Point Process by Minimum Contrast | |

summary.solist | Summary of a List of Spatial Objects | |

unstack.msr | Separate a Vector Measure into its Scalar Components | |

rPoissonCluster | Simulate Poisson Cluster Process | |

print.ppp | Print Brief Details of a Point Pattern Dataset | |

quasirandom | Quasirandom Patterns | |

quadrat.test.splitppp | Dispersion Test of CSR for Split Point Pattern Based on Quadrat Counts | |

quad.object | Class of Quadrature Schemes | |

subspaceDistance | Distance Between Linear Spaces | |

rQuasi | Generate Quasirandom Point Pattern in Given Window | |

ragsAreaInter | Alternating Gibbs Sampler for Area-Interaction Process | |

quadratcount | Quadrat counting for a point pattern | |

rCauchy | Simulate Neyman-Scott Point Process with Cauchy cluster kernel | |

ragsMultiHard | Alternating Gibbs Sampler for Multitype Hard Core Process | |

rho2hat | Smoothed Relative Density of Pairs of Covariate Values | |

runifdisc | Generate N Uniform Random Points in a Disc | |

rdpp | Simulation of a Determinantal Point Process | |

summary.quad | Summarizing a Quadrature Scheme | |

timed | Record the Computation Time | |

update.interact | Update an Interpoint Interaction | |

transect.im | Pixel Values Along a Transect | |

psp | Create a Line Segment Pattern | |

rHardcore | Perfect Simulation of the Hardcore Process | |

rmhmodel.list | Define Point Process Model for Metropolis-Hastings Simulation. | |

rthin | Random Thinning | |

quadrats | Divide Region into Quadrats | |

tiles | Extract List of Tiles in a Tessellation | |

sdr | Sufficient Dimension Reduction | |

rmhexpand | Specify Simulation Window or Expansion Rule | |

rmhcontrol | Set Control Parameters for Metropolis-Hastings Algorithm. | |

rstrat | Simulate Stratified Random Point Pattern | |

rGaussPoisson | Simulate Gauss-Poisson Process | |

rat | Ratio object | |

rMosaicSet | Mosaic Random Set | |

redwood | California Redwoods Point Pattern (Ripley's Subset) | |

rpoisline | Generate Poisson Random Line Process | |

rpoislinetess | Poisson Line Tessellation | |

solapply | Apply a Function Over a List and Obtain a List of Objects | |

runiflpp | Uniform Random Points on a Linear Network | |

vcov.kppm | Variance-Covariance Matrix for a Fitted Cluster Point Process Model | |

unstack.ppp | Separate Multiple Columns of Marks | |

where.max | Find Location of Maximum in a Pixel Image | |

update.detpointprocfamily | Set Parameter Values in a Determinantal Point Process Model | |

relevel.im | Reorder Levels of a Factor-Valued Image or Pattern | |

reach | Interaction Distance of a Point Process | |

reload.or.compute | Compute Unless Previously Saved | |

rgbim | Create Colour-Valued Pixel Image | |

rnoise | Random Pixel Noise | |

vcov.mppm | Calculate Variance-Covariance Matrix for Fitted Multiple Point Process Model | |

zapsmall.im | Rounding of Pixel Values | |

yardstick | Text, Arrow or Scale Bar in a Diagram | |

whichhalfplane | Test Which Side of Infinite Line a Point Falls On | |

raster.x | Cartesian Coordinates for a Pixel Raster | |

redwoodfull | California Redwoods Point Pattern (Entire Dataset) | |

round.ppp | Apply Numerical Rounding to Spatial Coordinates | |

residuals.dppm | Residuals for Fitted Determinantal Point Process Model | |

rsyst | Simulate systematic random point pattern | |

runifpoint | Generate N Uniform Random Points | |

subset.hyperframe | Subset of Hyperframe Satisfying A Condition | |

shift.im | Apply Vector Translation To Pixel Image | |

relrisk.ppp | Nonparametric Estimate of Spatially-Varying Relative Risk | |

requireversion | Require a Specific Version of a Package | |

rmh | Simulate point patterns using the Metropolis-Hastings algorithm. | |

rtemper | Simulated Annealing or Simulated Tempering for Gibbs Point Processes | |

slrm | Spatial Logistic Regression | |

sdrPredict | Compute Predictors from Sufficient Dimension Reduction | |

superimpose.lpp | Superimpose Several Point Patterns on Linear Network | |

shift.owin | Apply Vector Translation To Window | |

spatstat-package | The Spatstat Package | |

spruces | Spruces Point Pattern | |

spatstat.options | Internal Options in Spatstat Package | |

square | Square Window | |

subset.ppp | Subset of Point Pattern Satisfying A Condition | |

vesicles | Vesicles Data | |

valid.ppm | Check Whether Point Process Model is Valid | |

rlpp | Random Points on a Linear Network | |

shift.ppp | Apply Vector Translation To Point Pattern | |

rmhmodel.ppm | Interpret Fitted Model for Metropolis-Hastings Simulation. | |

rotmean | Rotational Average of a Pixel Image | |

superimpose | Superimpose Several Geometric Patterns | |

tile.areas | Compute Areas of Tiles in a Tessellation | |

tilenames | Names of Tiles in a Tessellation | |

tileindex | Determine Which Tile Contains Each Given Point | |

tweak.colourmap | Change Colour Values in a Colour Map | |

union.quad | Union of Data and Dummy Points | |

vertices | Vertices of a Window | |

valid.detpointprocfamily | Check Validity of a Determinantal Point Process Model | |

weighted.median | Weighted Median, Quantiles or Variance | |

rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |

summary.listof | Summary of a List of Things | |

rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |

scaletointerval | Rescale Data to Lie Between Specified Limits | |

waterstriders | Waterstriders data. Three independent replications of a point pattern formed by insects. | |

scan.test | Spatial Scan Test | |

setcov | Set Covariance of a Window | |

shapley | Galaxies in the Shapley Supercluster | |

texturemap | Texture Map | |

spiders | Spider Webs on Mortar Lines of a Brick Wall | |

simulate.ppm | Simulate a Fitted Gibbs Point Process Model | |

tess | Create a Tessellation | |

rlinegrid | Generate grid of parallel lines with random displacement | |

rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |

rlabel | Random Re-Labelling of Point Pattern | |

selfcrossing.psp | Crossing Points in a Line Segment Pattern | |

rmpoispp | Generate Multitype Poisson Point Pattern | |

run.simplepanel | Run Point-and-Click Interface | |

shift.psp | Apply Vector Translation To Line Segment Pattern | |

simulate.slrm | Simulate a Fitted Spatial Logistic Regression Model | |

unnormdensity | Weighted kernel smoother | |

split.ppp | Divide Point Pattern into Sub-patterns | |

rotate.ppp | Rotate a Point Pattern | |

rotate.psp | Rotate a Line Segment Pattern | |

rppm | Recursively Partitioned Point Process Model | |

simplify.owin | Approximate a Polygon by a Simpler Polygon | |

ssf | Spatially Sampled Function | |

segregation.test | Test of Spatial Segregation of Types | |

scanLRTS | Likelihood Ratio Test Statistic for Scan Test | |

split.hyperframe | Divide Hyperframe Into Subsets and Reassemble | |

split.ppx | Divide Multidimensional Point Pattern into Sub-patterns | |

tiles.empty | Check For Empty Tiles in a Tessellation | |

summary.kppm | Summarizing a Fitted Cox or Cluster Point Process Model | |

summary.ppp | Summary of a Point Pattern Dataset | |

textureplot | Plot Image or Tessellation Using Texture Fill | |

split.msr | Divide a Measure into Parts | |

solist | List of Two-Dimensional Spatial Objects | |

timeTaken | Extract the Total Computation Time | |

summary.psp | Summary of a Line Segment Pattern Dataset | |

volume | Volume of an Object | |

unmark | Remove Marks | |

update.rmhcontrol | Update Control Parameters of Metropolis-Hastings Algorithm | |

varcount | Predicted Variance of the Number of Points | |

varblock | Estimate Variance of Summary Statistic by Subdivision | |

waka | Trees in Waka national park | |

update.symbolmap | Update a Graphics Symbol Map. | |

zclustermodel | Cluster Point Process Model | |

simplepanel | Simple Point-and-Click Interface Panels | |

solutionset | Evaluate Logical Expression Involving Pixel Images and Return Region Where Expression is True | |

sumouter | Compute Quadratic Forms | |

split.im | Divide Image Into Sub-images | |

summary.anylist | Summary of a List of Things | |

summary.im | Summarizing a Pixel Image | |

summary.splitppp | Summary of a Split Point Pattern | |

stieltjes | Compute Integral of Function Against Cumulative Distribution | |

transmat | Convert Pixel Array Between Different Conventions | |

update.ppm | Update a Fitted Point Process Model | |

vcov.ppm | Variance-Covariance Matrix for a Fitted Point Process Model | |

thinNetwork | Remove Vertices or Segments from a Linear Network | |

trim.rectangle | Cut margins from rectangle | |

treebranchlabels | Label Vertices of a Tree by Branch Membership | |

triplet.family | Triplet Interaction Family | |

with.hyperframe | Evaluate an Expression in Each Row of a Hyperframe | |

update.kppm | Update a Fitted Cluster Point Process Model | |

vcov.slrm | Variance-Covariance Matrix for a Fitted Spatial Logistic Regression | |

with.fv | Evaluate an Expression in a Function Table | |

No Results! |

## Vignettes of spatstat

Name | ||

datasets.Rnw | ||

getstart.Rnw | ||

hexagon.eps | ||

hexagon.pdf | ||

irregpoly.eps | ||

irregpoly.pdf | ||

replicated.Rnw | ||

shapefiles.Rnw | ||

updates.Rnw | ||

No Results! |

## Last month downloads

## Details

Nickname | Apophenia |

Date | 2017-08-16 |

Remotes | spatstat/spatstat.utils |

License | GPL (>= 2) |

URL | http://www.spatstat.org |

LazyData | true |

NeedsCompilation | yes |

ByteCompile | true |

BugReports | https://github.com/spatstat/spatstat/issues |

Packaged | 2017-08-16 06:41:56 UTC; adrian |

Repository | CRAN |

Date/Publication | 2017-08-16 09:01:38 UTC |

imports | abind , deldir (>= 0.0-21) , goftest , Matrix , mgcv , polyclip (>= 1.5-0) , spatstat.utils (>= 1.7-0) , tensor |

suggests | fftwtools (>= 0.9-8) , gsl , locfit , maptools , RandomFields (>= 3.1.24.1) , RandomFieldsUtils (>= 0.3.3.1) , rpanel , sm , spatial , tkrplot |

depends | graphics , grDevices , methods , nlme , R (>= 3.3.0) , rpart , stats , utils |

Contributors | Adrian Baddeley, Rolf Turner, Ege Rubak |

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