# spatstat v1.64-1

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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()) 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.

## Functions in spatstat

Name | Description | |

CDF | Cumulative Distribution Function From Kernel Density Estimate | |

BadGey | Hybrid Geyer Point Process Model | |

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

Emark | Diagnostics for random marking | |

DiggleGratton | Diggle-Gratton model | |

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

AreaInter | The Area Interaction Point Process Model | |

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

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

Extract.owin | Extract Subset of Window | |

Extract.linnet | Extract Subset of Linear Network | |

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

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

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

Extract.im | Extract Subset of Image | |

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

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

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

Concom | The Connected Component Process Model | |

[.ssf | Subset of spatially sampled function | |

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

Extract.fasp | Extract Subset of Function Array | |

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

Extract.quad | Subset of Quadrature Scheme | |

Fiksel | The Fiksel Interaction | |

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

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

Hardcore | The Hard Core Point Process Model | |

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

Jinhom | Inhomogeneous J-function | |

Hest | Spherical Contact Distribution Function | |

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

Gest | Nearest Neighbour Distance Function G | |

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

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

Gmulti | Marked Nearest Neighbour Distance Function | |

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

Jmulti | Marked J Function | |

Ginhom | Inhomogeneous Nearest Neighbour Function | |

Geyer | Geyer's Saturation Point Process Model | |

Hybrid | Hybrid Interaction Point Process Model | |

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

LambertW | Lambert's W Function | |

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

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

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

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

Kmulti | Marked K-Function | |

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

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

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

Kmeasure | Reduced Second Moment Measure | |

Gfox | Foxall's Distance Functions | |

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

Kdot.inhom | Inhomogeneous Multitype K Dot Function | |

HierHard | The Hierarchical Hard Core Point Process Model | |

HierStrauss | The Hierarchical Strauss Point Process Model | |

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

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

Gcom | Model Compensator of Nearest Neighbour Function | |

Finhom | Inhomogeneous Empty Space Function | |

FmultiInhom | Inhomogeneous Marked F-Function | |

Iest | Estimate the I-function | |

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

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

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

Pairwise | Generic Pairwise Interaction model | |

Penttinen | Penttinen Interaction | |

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

MinkowskiSum | Minkowski Sum of Windows | |

Ops.msr | Arithmetic Operations on Measures | |

Kmark | Mark-Weighted K Function | |

Kinhom | Inhomogeneous K-function | |

Kcross.inhom | Inhomogeneous Cross K Function | |

Kcom | Model Compensator of K Function | |

Kscaled | Locally Scaled K-function | |

Kmulti.inhom | Inhomogeneous Marked K-Function | |

Gres | Residual G Function | |

GmultiInhom | Inhomogeneous Marked G-Function | |

Lest | L-function | |

LennardJones | The Lennard-Jones Potential | |

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

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

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

Ldot.inhom | Inhomogeneous Multitype L Dot Function | |

Ksector | Sector K-function | |

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

Kres | Residual K Function | |

OrdThresh | Ord's Interaction model | |

Linhom | Inhomogeneous L-function | |

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

Triplets | The Triplet Point Process Model | |

Smooth.ssf | Smooth a Spatially Sampled Function | |

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

Ord | Generic Ord Interaction model | |

affine.owin | Apply Affine Transformation To Window | |

Replace.linim | Reset Values in Subset of Image on Linear Network | |

Tstat | Third order summary statistic | |

Smooth.fv | Apply Smoothing to Function Values | |

Jest | Estimate the J-function | |

Poisson | Poisson Point Process Model | |

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

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

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

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

Softcore | The Soft Core Point Process Model | |

Kest.fft | K-function using FFT | |

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

affine | Apply Affine Transformation | |

Kest | K-function | |

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

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

addvar | Added Variable Plot for Point Process Model | |

addVertices | Add New Vertices to a Linear Network | |

Lcross.inhom | Inhomogeneous Cross Type L Function | |

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

add.texture | Fill Plot With Texture | |

adaptive.density | Adaptive Estimate of Intensity of Point Pattern | |

angles.psp | Orientation Angles of Line Segments | |

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

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

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

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

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

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

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

areaGain | Difference of Disc Areas | |

area.owin | Area of a Window | |

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

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

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

WindowOnly | Extract Window of Spatial Object | |

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

append.psp | Combine Two Line Segment Patterns | |

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

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

as.data.frame.lintess | Convert Network Tessellation to Data Frame | |

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

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

areaLoss | Difference of Disc Areas | |

MultiHard | The Multitype Hard Core Point Process Model | |

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

anylist | List of Objects | |

Saturated | Saturated Pairwise Interaction model | |

MultiStrauss | The Multitype Strauss Point Process Model | |

as.im | Convert to Pixel Image | |

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

Smooth | Spatial smoothing of data | |

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

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

as.fv | Convert Data To Class fv | |

beginner | Print Introduction For Beginners | |

as.ppp | Convert Data To Class ppp | |

as.hyperframe | Convert Data to Hyperframe | |

Strauss | The Strauss Point Process Model | |

affine.tess | Apply Geometrical Transformation To Tessellation | |

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

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

as.ppm | Extract Fitted Point Process Model | |

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

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

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

as.layered | Convert Data To Layered Object | |

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

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

as.interact | Extract Interaction Structure | |

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

as.rectangle | Window Frame | |

as.tess | Convert Data To Tessellation | |

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

begins | Check Start of Character String | |

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

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

as.psp | Convert Data To Class psp | |

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

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

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

blur | Apply Gaussian Blur to a Pixel Image | |

bw.abram | Abramson's Adaptive Bandwidths | |

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

boundingcircle | Smallest Enclosing Circle | |

auc | Area Under ROC Curve | |

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

bc.ppm | Bias Correction for Fitted Model | |

bdist.pixels | Distance to Boundary of Window | |

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

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

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

bugfixes | List Recent Bug Fixes | |

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

bw.CvL | Cronie and van Lieshout's Criterion for Bandwidth Selection for Kernel Density | |

box3 | Three-Dimensional Box | |

border | Border Region of a Window | |

bdist.points | Distance to Boundary of Window | |

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

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

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

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

as.owin | Convert Data To Class owin | |

bind.fv | Combine Function Value Tables | |

circdensity | Density Estimation for Circular Data | |

bw.voronoi | Cross Validated Bandwidth Selection for Voronoi Estimator of Intensity on a Network | |

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

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

clickbox | Interactively Define a Rectangle | |

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

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

bw.lppl | Likelihood Cross Validation Bandwidth Selection for Kernel Density on a Linear Network | |

bits.envelope | Global Envelopes for Balanced Independent Two-Stage Test | |

clarkevans | Clark and Evans Aggregation Index | |

bdist.tiles | Distance to Boundary of Window | |

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

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

clusterkernel | Extract Cluster Offspring Kernel | |

clickjoin | Interactively join vertices on a plot | |

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

clarkevans.test | Clark and Evans Test | |

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

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

clickppp | Interactively Add Points | |

chop.linnet | Divide a Linear Network into Tiles Using Infinite Lines | |

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

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

clickdist | Interactively Measure Distance | |

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

closepairs | Close Pairs of Points | |

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

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

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

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

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

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

colourtools | Convert and Compare Colours in Different Formats | |

colourmap | Colour Lookup Tables | |

branchlabelfun | Tree Branch Membership Labelling Function | |

bw.relrisklpp | Cross Validated Bandwidth Selection for Relative Risk Estimation on a Network | |

colouroutputs | Extract or Assign Colour Values in a Colour Map | |

boxx | Multi-Dimensional Box | |

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

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

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

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

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

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

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

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

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

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

closetriples | Close Triples of Points | |

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

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

complement.owin | Take Complement of a Window | |

clusterset | Allard-Fraley Estimator of Cluster Feature | |

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

convexhull | Convex Hull | |

closing | Morphological Closing | |

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

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

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

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

centroid.owin | Centroid of a window | |

clicklpp | Interactively Add Points on a Linear Network | |

clickpoly | Interactively Define a Polygon | |

convexhull.xy | Convex Hull of Points | |

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

clusterfield | Field of clusters | |

compatible | Test Whether Objects Are Compatible | |

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

connected.tess | Connected Components of Tiles of a Tessellation | |

convexify | Weil's Convexifying Operation | |

compareFit | Residual Diagnostics for Multiple Fitted Models | |

convolve.im | Convolution of Pixel Images | |

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

delaunay | Delaunay Triangulation of Point Pattern | |

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

density.psp | Kernel Smoothing of Line Segment Pattern or Linear Network | |

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

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

densityVoronoi | Intensity Estimate of Point Pattern Using Voronoi-Dirichlet Tessellation | |

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

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

delaunayDistance | Distance on Delaunay Triangulation | |

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

densityfun.lpp | Kernel Estimate of Intensity on a Linear Network as a Spatial Function | |

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

connected | Connected components | |

covering | Cover Region with Discs | |

concatxy | Concatenate x,y Coordinate Vectors | |

densityVoronoi.lpp | Intensity Estimate of Point Pattern on Linear Network Using Voronoi-Dirichlet Tessellation | |

densityfun.ppp | Kernel Estimate of Intensity as a Spatial Function | |

contour.im | Contour plot of pixel image | |

corners | Corners of a rectangle | |

diameter | Diameter of an Object | |

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

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

contour.imlist | Array of Contour Plots | |

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

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

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

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

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

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

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

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

data.lppm | Extract Original Data from a Fitted Point Process Model on a Network | |

crossdist | Pairwise distances | |

dilated.areas | Areas of Morphological Dilations | |

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

dilation | Morphological Dilation | |

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

deltametric | Delta Metric | |

densityEqualSplit | Equal-Split Algorithm for Kernel Density on a Network | |

densityAdaptiveKernel | Adaptive Kernel Estimate of Intensity of Point Pattern | |

dirichletVertices | Vertices and Edges of Dirichlet Tessellation | |

dirichletWeights | Compute Quadrature Weights Based on Dirichlet Tessellation | |

distfun.lpp | Distance Map on Linear Network | |

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

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

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

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

dppMatern | Whittle-Matern Determinantal Point Process Model | |

densityHeat | Kernel Density on a Network using Heat Equation | |

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

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

dppPowerExp | Power Exponential Spectral Determinantal Point Process Model | |

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

distmap | Distance Map | |

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

dimhat | Estimate Dimension of Central Subspace | |

domain | Extract the Domain of any Spatial Object | |

deriv.fv | Calculate Derivative of Function Values | |

dffit.ppm | Case Deletion Effect Measure of Fitted Model | |

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

densityQuick.lpp | Kernel Estimation of Intensity on a Network using a 2D Kernel | |

distcdf | Distribution Function of Interpoint Distance | |

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

emend | Force Model to be Valid | |

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

dppBessel | Bessel Type Determinantal Point Process Model | |

discs | Union of Discs | |

dppapproxkernel | Approximate Determinantal Point Process Kernel | |

distfun | Distance Map as a Function | |

ellipse | Elliptical Window. | |

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

distmap.owin | Distance Map of Window | |

diameter.owin | Diameter of a Window | |

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

dfbetas.ppm | Parameter Influence Measure | |

distmap.ppp | Distance Map of Point Pattern | |

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

dppeigen | Internal function calculating eig and index | |

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

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

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

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

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

dirichletAreas | Compute Areas of Tiles in Dirichlet Tessellation | |

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

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

disc | Circular Window | |

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

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

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

dirichlet | Dirichlet Tessellation of Point Pattern | |

discpartarea | Area of Part of Disc | |

dppCauchy | Generalized Cauchy Determinantal Point Process Model | |

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

dppGauss | Gaussian Determinantal Point Process Model | |

dkernel | Kernel distributions and random generation | |

edges2triangles | List Triangles in a Graph | |

dppm | Fit Determinantal Point Process Model | |

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

edges | Extract Boundary Edges of a Window. | |

edges2vees | List Dihedral Triples in a Graph | |

edge.Trans | Translation Edge Correction | |

erosion | Morphological Erosion by a Disc | |

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

grow.rectangle | Add margins to rectangle | |

edit.hyperframe | Invoke Text Editor on Hyperframe | |

eem | Exponential Energy Marks | |

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

eval.fasp | Evaluate Expression Involving Function Arrays | |

harmonic | Basis for Harmonic Functions | |

endpoints.psp | Endpoints of Line Segment Pattern | |

eval.fv | Evaluate Expression Involving Functions | |

envelope | Simulation Envelopes of Summary Function | |

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

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

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

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

erosionAny | Morphological Erosion of Windows | |

eval.im | Evaluate Expression Involving Pixel Images | |

ewcdf | Weighted Empirical Cumulative Distribution Function | |

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

foo | Foo is Not a Real Name | |

fardist | Farthest Distance to Boundary of Window | |

flipxy | Exchange X and Y Coordinates | |

dmixpois | Mixed Poisson Distribution | |

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

harmonise | Make Objects Compatible | |

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

fasp.object | Function Arrays for Spatial Patterns | |

envelope.envelope | Recompute Envelopes | |

fourierbasis | Fourier Basis Functions | |

funxy | Spatial Function Class | |

expand.owin | Apply Expansion Rule | |

harmonise.fv | Make Function Tables Compatible | |

envelopeArray | Array of Simulation Envelopes of Summary Function | |

im.object | Class of Images | |

eroded.areas | Areas of Morphological Erosions | |

heatkernelapprox | Approximation to Heat Kernel on Linear Network at Source Point | |

gridweights | Compute Quadrature Weights Based on Grid Counts | |

hextess | Hexagonal Grid or Tessellation | |

hierpair.family | Hierarchical Pairwise Interaction Process Family | |

fv | Create a Function Value Table | |

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

imcov | Spatial Covariance of a Pixel Image | |

extrapolate.psp | Extrapolate Line Segments to Obtain Infinite Lines | |

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

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

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

hopskel | Hopkins-Skellam Test | |

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

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

harmonise.msr | Make Measures Compatible | |

intensity.ppp | Empirical Intensity of Point Pattern | |

harmonise.im | Make Pixel Images Compatible | |

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

fryplot | Fry Plot of Point Pattern | |

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

hotrod | Heat Kernel for a One-Dimensional Rod | |

hybrid.family | Hybrid Interaction Family | |

incircle | Find Largest Circle Inside Window | |

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

fv.object | Function Value Table | |

increment.fv | Increments of a Function | |

gridcentres | Rectangular grid of points | |

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

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

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

integral.msr | Integral of a Measure | |

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

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

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

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

hyperframe | Hyper Data Frame | |

intensity | Intensity of a Dataset or a Model | |

harmonise.owin | Make Windows Compatible | |

infline | Infinite Straight Lines | |

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

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

laslett | Laslett's Transform | |

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

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

intersect.tess | Intersection of Two Tessellations | |

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

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

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

intensity.psp | Empirical Intensity of Line Segment Pattern | |

levelset | Level Set of a Pixel Image | |

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

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

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

im | Create a Pixel Image Object | |

inforder.family | Infinite Order Interaction Family | |

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

intersect.lintess | Intersection of Tessellations on a Linear Network | |

integral.linim | Integral on a Linear Network | |

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

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

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

integral.im | Integral of a Pixel Image | |

linnet | Create a Linear Network | |

interp.colourmap | Interpolate smoothly between specified colours | |

insertVertices | Insert New Vertices in a Linear Network | |

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

interp.im | Interpolate a Pixel Image | |

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

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

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

intensity.quadratcount | Intensity Estimates Using Quadrat Counts | |

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

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

pairs.im | Scatterplot Matrix for Pixel Images | |

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

lintess | Tessellation on a Linear Network | |

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

joinVertices | Join Vertices in a Network | |

localpcf | Local pair correlation function | |

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

is.rectangle | Determine Type of Window | |

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

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

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

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

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

kernel.moment | Moment of Smoothing Kernel | |

is.marked | Test Whether Marks Are Present | |

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

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

is.multitype | Test whether Object is Multitype | |

kppm | Fit Cluster or Cox Point Process Model | |

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

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

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

kernel.squint | Integral of Squared Kernel | |

lurking | Lurking Variable Plot | |

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

matrixpower | Power of a Matrix | |

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

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

lineardirichlet | Dirichlet Tessellation on a Linear Network | |

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

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

measureContinuous | Discrete and Continuous Components of a Measure | |

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

is.linim | Test Whether an Object is a Pixel Image on a Linear Network | |

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

linearpcf | Linear Pair Correlation Function | |

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

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

methods.fii | Methods for Fitted Interactions | |

layered | Create List of Plotting Layers | |

linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |

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

kernel.factor | Scale factor for density kernel | |

linearK | Linear K Function | |

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

pairdist | Pairwise distances | |

miplot | Morisita Index Plot | |

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

methods.layered | Methods for Layered Objects | |

lineartileindex | Determine Which Tile Contains Each Given Point on a Linear Network | |

localKdot | Local Multitype K Function (Dot-Type) | |

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

linequad | Quadrature Scheme on a Linear Network | |

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

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

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

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

logLik.slrm | Loglikelihood of Spatial Logistic Regression | |

lixellate | Subdivide Segments of a Network | |

marks.tess | Marks of a Tessellation | |

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

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

markcorr | Mark Correlation Function | |

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

localK | Neighbourhood density function | |

methods.distfun | Geometrical Operations for Distance Functions | |

lohboot | Bootstrap Confidence Bands for Summary Function | |

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

lurking.mppm | Lurking Variable Plot for Multiple Point Patterns | |

localKinhom | Inhomogeneous Neighbourhood Density Function | |

lpp | Create Point Pattern on Linear Network | |

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

linearKinhom | Inhomogeneous Linear K Function | |

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

lengths_psp | Lengths of Line Segments | |

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

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

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

model.images | Compute Images of Constructed Covariates | |

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

localKcross | Local Multitype K Function (Cross-Type) | |

linfun | Function on a Linear Network | |

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

methods.unitname | Methods for Units | |

localKcross.inhom | Inhomogeneous Multitype K Function | |

linim | Create Pixel Image on Linear Network | |

lut | Lookup Tables | |

mergeLevels | Merge Levels of a Factor | |

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

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

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

pairdist.ppp | Pairwise distances | |

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

midpoints.psp | Midpoints of Line Segment Pattern | |

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

nnwhich.pp3 | Nearest neighbours in three dimensions | |

methods.linnet | Methods for Linear Networks | |

methods.objsurf | Methods for Objective Function Surfaces | |

methods.funxy | Methods for Spatial Functions | |

pairdist.psp | Pairwise distances between line segments | |

model.matrix.mppm | Extract Design Matrix of Point Process Model for Several Point Patterns | |

matchingdist | Distance for a Point Pattern Matching | |

markcrosscorr | Mark Cross-Correlation Function | |

markconnect | Mark Connection Function | |

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

markmarkscatter | Mark-Mark Scatter Plot | |

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

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

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

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

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

pointsOnLines | Place Points Evenly Along Specified Lines | |

nnfun | Nearest Neighbour Index Map as a Function | |

markvario | Mark Variogram | |

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

npoints | Number of Points in a Point Pattern | |

plot.owin | Plot a Spatial Window | |

nncross.ppx | Nearest Neighbours Between Two Patterns in Any Dimensions | |

mincontrast | Method of Minimum Contrast | |

nnclean | Nearest Neighbour Clutter Removal | |

marks | Marks of a Point Pattern | |

methods.ssf | Methods for Spatially Sampled Functions | |

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

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

plot.ppp | plot a Spatial Point Pattern | |

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

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

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

owin.object | Class owin | |

padimage | Pad the Border of a Pixel Image | |

pixelcentres | Extract Pixel Centres as Point Pattern | |

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

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

nnwhich.ppx | Nearest Neighbours in Any Dimensions | |

nestsplit | Nested Split | |

nnfromvertex | Nearest Data Point From Each Vertex in a Network | |

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

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

multiplicity.ppp | Count Multiplicity of Duplicate Points | |

msr | Signed or Vector-Valued Measure | |

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

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

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

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

ppx | Multidimensional Space-Time Point Pattern | |

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

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

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

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

pairdist.default | Pairwise distances | |

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

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

nearestsegment | Find Line Segment Nearest to Each Point | |

ord.family | Ord Interaction Process Family | |

nncross | Nearest Neighbours Between Two Patterns | |

methods.zclustermodel | Methods for Cluster Models | |

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

plot.colourmap | Plot a Colour Map | |

pairdist.pp3 | Pairwise distances in Three Dimensions | |

nnmap | K-th Nearest Point Map | |

plot.cdftest | Plot a Spatial Distribution Test | |

nearestValue | Image of Nearest Defined Pixel Value | |

nnmark | Mark of Nearest Neighbour | |

parres | Partial Residuals for Point Process Model | |

nnorient | Nearest Neighbour Orientation Distribution | |

nnwhich | Nearest neighbour | |

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

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

pairwise.family | Pairwise Interaction Process Family | |

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

pairorient | Point Pair Orientation Distribution | |

pixellate | Convert Spatial Object to Pixel Image | |

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

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

plot.fasp | Plot a Function Array | |

opening | Morphological Opening | |

pixelquad | Quadrature Scheme Based on Pixel Grid | |

npfun | Dummy Function Returns Number of Points | |

nndist | Nearest neighbour distances | |

owin | Create a Window | |

overlap.owin | Compute Area of Overlap | |

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

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

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

pairdist.ppx | Pairwise Distances in Any Dimensions | |

plot.hyperframe | Plot Entries in a Hyperframe | |

pcf | Pair Correlation Function | |

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

plot.pppmatching | Plot a Point Matching | |

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

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

plot.laslett | Plot Laslett Transform | |

pppdist | Distance Between Two Point Patterns | |

plot.anylist | Plot a List of Things | |

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

perimeter | Perimeter Length of Window | |

plot.layered | Layered Plot | |

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

plot.onearrow | Plot an Arrow | |

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

pixellate.owin | Convert Window to Pixel Image | |

persp.im | Perspective Plot of Pixel Image | |

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

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

nvertices | Count Number of Vertices | |

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

objsurf | Objective Function Surface | |

plot.texturemap | Plot a Texture Map | |

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

plot.im | Plot a Pixel Image | |

rThomas | Simulate Thomas Process | |

points.lpp | Draw Points on Existing Plot | |

periodify | Make Periodic Copies of a Spatial Pattern | |

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

plot.leverage.ppm | Plot Leverage Function | |

pcfmulti | Marked pair correlation function | |

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

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

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

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

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

relrisk.lpp | Nonparametric Estimate of Spatially-Varying Relative Risk on a Network | |

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

plot.influence.ppm | Plot Influence Measure | |

pcfinhom | Inhomogeneous Pair Correlation Function | |

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

pool.fv | Pool Several Functions | |

parameters | Extract Model Parameters in Understandable Form | |

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

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

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

plot.listof | Plot a List of Things | |

plot.quad | Plot a Spatial Quadrature Scheme | |

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

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

project2set | Find Nearest Point in a Region | |

plot.studpermutest | Plot a Studentised Permutation Test | |

plot.textstring | Plot a Text String | |

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

plot.tess | Plot a Tessellation | |

project2segment | Move Point To Nearest Line | |

plot.bermantest | Plot Result of Berman Test | |

plot.envelope | Plot a Simulation Envelope | |

plot.imlist | Plot a List of Images | |

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

plot.linnet | Plot a linear network | |

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

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

pool.quadrattest | Pool Several Quadrat Tests | |

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

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

ppm | Fit Point Process Model to Data | |

plot.quadratcount | Plot Quadrat Counts | |

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

plot.profilepl | Plot Profile Likelihood | |

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

pool | Pool Data | |

plot.symbolmap | Plot a Graphics Symbol Map | |

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

plot.ssf | Plot a Spatially Sampled Function | |

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

quadratresample | Resample a Point Pattern by Resampling Quadrats | |

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

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

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

plot.fv | Plot Function Values | |

pool.envelope | Pool Data from Several Envelopes | |

quadratcount | Quadrat counting for a point pattern | |

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

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

pppmatching.object | Class of Point Matchings | |

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

print.quad | Print a Quadrature Scheme | |

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

rMatClust | Simulate Matern Cluster Process | |

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

quantile.density | Quantiles of a Density Estimate | |

pp3 | Three Dimensional Point Pattern | |

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

polartess | Tessellation Using Polar Coordinates | |

quadrats | Divide Region into Quadrats | |

progressreport | Print Progress Reports | |

ppp | Create a Point Pattern | |

rSSI | Simulate Simple Sequential Inhibition | |

psp.object | Class of Line Segment Patterns | |

quantess | Quantile Tessellation | |

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

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

psp | Create a Line Segment Pattern | |

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

rGaussPoisson | Simulate Gauss-Poisson Process | |

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

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

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

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

rescale | Convert dataset to another unit of length | |

psib | Sibling Probability of Cluster Point Process | |

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

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

rMaternII | Simulate Matern Model II | |

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

rMaternI | Simulate Matern Model I | |

rHardcore | Perfect Simulation of the Hardcore Process | |

rStrauss | Perfect Simulation of the Strauss Process | |

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

rPoissonCluster | Simulate Poisson Cluster Process | |

rdpp | Simulation of a Determinantal Point Process | |

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

rLGCP | Simulate Log-Gaussian Cox Process | |

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

ppp.object | Class of Point Patterns | |

rQuasi | Generate Quasirandom Point Pattern in Given Window | |

polynom | Polynomial in One or Two Variables | |

reach | Interaction Distance of a Point Process | |

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

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

quantile.im | Sample Quantiles of Pixel Image | |

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

range.fv | Range of Function Values | |

ripras | Estimate window from points alone | |

quad.object | Class of Quadrature Schemes | |

rSwitzerlpp | Switzer-type Point Process on Linear Network | |

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

rose | Rose Diagram | |

reflect | Reflect In Origin | |

regularpolygon | Create A Regular Polygon | |

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

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

pppmatching | Create a Point Matching | |

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

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

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

quasirandom | Quasirandom Patterns | |

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

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

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

relrisk | Estimate of Spatially-Varying Relative Risk | |

repairNetwork | Repair Internal Data in a Linear Network | |

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

rmhexpand | Specify Simulation Window or Expansion Rule | |

rectdistmap | Distance Map Using Rectangular Distance Metric | |

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

rounding | Detect Numerical Rounding | |

rotate.infline | Rotate or Shift Infinite Lines | |

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

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

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

rcelllpp | Simulate Cell Process on Linear Network | |

rMosaicSet | Mosaic Random Set | |

rags | Alternating Gibbs Sampler for Multitype Point Processes | |

rMosaicField | Mosaic Random Field | |

rlpp | Random Points on a Linear Network | |

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

rshift.ppp | Randomly Shift a Point Pattern | |

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

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

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

rotate | Rotate | |

rppm | Recursively Partitioned Point Process Model | |

rgbim | Create Colour-Valued Pixel Image | |

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

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

rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |

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

rjitter | Random Perturbation of a Point Pattern | |

reach.kppm | Range of Interaction for a Cox or Cluster Point Process Model | |

rotate.im | Rotate a Pixel Image | |

repul.dppm | Repulsiveness Index of a Determinantal Point Process Model | |

rshift | Random Shift | |

rectcontact | Contact Distribution Function using Rectangular Structuring Element | |

rmpoispp | Generate Multitype Poisson Point Pattern | |

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

rNeymanScott | Simulate Neyman-Scott Process | |

rex | Richardson Extrapolation | |

scanLRTS | Likelihood Ratio Test Statistic for Scan Test | |

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

rthinclumps | Random Thinning of Clumps | |

rPenttinen | Perfect Simulation of the Penttinen Process | |

rknn | Theoretical Distribution of Nearest Neighbour Distance | |

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

rthin | Random Thinning | |

rnoise | Random Pixel Noise | |

requireversion | Require a Specific Version of a Package | |

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

rpoint | Generate N Random Points | |

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

rlabel | Random Re-Labelling of Point Pattern | |

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

scalardilate | Apply Scalar Dilation | |

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

roc | Receiver Operating Characteristic | |

rmpoint | Generate N Random Multitype Points | |

rotate.owin | Rotate a Window | |

rat | Ratio object | |

rsyst | Simulate systematic random point pattern | |

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

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

rotate.ppp | Rotate a Point Pattern | |

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

rotate.psp | Rotate a Line Segment Pattern | |

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

rpoispp | Generate Poisson Point Pattern | |

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

rcell | Simulate Baddeley-Silverman Cell Process | |

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

rescue.rectangle | Convert Window Back To Rectangle | |

rpoisline | Generate Poisson Random Line Process | |

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

rpoislinetess | Poisson Line Tessellation | |

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

rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |

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

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

setcov | Set Covariance of a Window | |

rotmean | Rotational Average of a Pixel Image | |

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

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

rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |

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

rpoislpp | Poisson Point Process on a Linear Network | |

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

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

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

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

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

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

sharpen | Data Sharpening of Point Pattern | |

rstrat | Simulate Stratified Random Point Pattern | |

shift | Apply Vector Translation | |

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

scanpp | Read Point Pattern From Data File | |

runifpoint | Generate N Uniform Random Points | |

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

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

sdr | Sufficient Dimension Reduction | |

runiflpp | Uniform Random Points on a Linear Network | |

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

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

sdrPredict | Compute Predictors from Sufficient Dimension Reduction | |

scaletointerval | Rescale Data to Lie Between Specified Limits | |

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

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

scan.test | Spatial Scan Test | |

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

shift.owin | Apply Vector Translation To Window | |

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

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

No Results! |

## Vignettes of spatstat

Name | ||

bugfixes.Rnw | ||

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## Last month downloads

## Details

Date | 2020-05-10 |

License | GPL (>= 2) |

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

LazyData | true |

NeedsCompilation | yes |

ByteCompile | true |

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

Packaged | 2020-05-10 03:00:50 UTC; adrian |

Repository | CRAN |

Date/Publication | 2020-05-12 17:10:02 UTC |

imports | abind , deldir (>= 0.0-21) , goftest (>= 1.2-2) , Matrix , mgcv , polyclip (>= 1.10-0) , spatstat.utils (>= 1.17-0) , tensor |

suggests | fftwtools (>= 0.9-8) , gsl , locfit , maptools (>= 0.9-9) , RandomFields (>= 3.1.24.1) , RandomFieldsUtils (>= 0.3.3.1) , sm , spatial |

depends | graphics , grDevices , methods , nlme , R (>= 3.3.0) , rpart , spatstat.data (>= 1.4-2) , stats , utils |

Contributors | Adrian Baddeley, Rolf Turner, Ege Rubak |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/spatstat)](http://www.rdocumentation.org/packages/spatstat)
```