# spatstat v1.62-2

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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 | |

BadGey | Hybrid Geyer Point Process Model | |

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

Emark | Diagnostics for random marking | |

Concom | The Connected Component Process Model | |

AreaInter | The Area Interaction Point Process Model | |

CDF | Cumulative Distribution Function From Kernel Density Estimate | |

DiggleGratton | Diggle-Gratton model | |

Extract.fasp | Extract Subset of Function Array | |

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

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

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

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

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

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

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

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

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

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

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

Extract.im | Extract Subset of Image | |

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

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

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

[.ssf | Subset of spatially sampled function | |

Extract.quad | Subset of Quadrature Scheme | |

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

Gcom | Model Compensator of Nearest Neighbour Function | |

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

GmultiInhom | Inhomogeneous Marked G-Function | |

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

Gres | Residual G Function | |

Finhom | Inhomogeneous Empty Space Function | |

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

FmultiInhom | Inhomogeneous Marked F-Function | |

Geyer | Geyer's Saturation Point Process Model | |

Gfox | Foxall's Distance Functions | |

Hardcore | The Hard Core Point Process Model | |

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

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

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

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

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

Kcom | Model Compensator of K Function | |

Hest | Spherical Contact Distribution Function | |

Kdot.inhom | Inhomogeneous Multitype K Dot Function | |

Extract.linnet | Extract Subset of Linear Network | |

Extract.owin | Extract Subset of Window | |

Gmulti | Marked Nearest Neighbour Distance Function | |

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

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

Hybrid | Hybrid Interaction Point Process Model | |

Kmeasure | Reduced Second Moment Measure | |

Fiksel | The Fiksel Interaction | |

Jinhom | Inhomogeneous J-function | |

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

HierStrauss | The Hierarchical Strauss Point Process Model | |

Jmulti | Marked J Function | |

HierHard | The Hierarchical Hard Core Point Process Model | |

Jest | Estimate the J-function | |

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

Kres | Residual K Function | |

LennardJones | The Lennard-Jones Potential | |

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

Ldot.inhom | Inhomogeneous Multitype L Dot Function | |

Kmulti.inhom | Inhomogeneous Marked K-Function | |

Ops.msr | Arithmetic Operations on Measures | |

Gest | Nearest Neighbour Distance Function G | |

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

Kmark | Mark-Weighted K Function | |

Ginhom | Inhomogeneous Nearest Neighbour Function | |

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

Ksector | Sector K-function | |

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

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

MultiHard | The Multitype Hard Core Point Process Model | |

Kscaled | Locally Scaled K-function | |

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

Kcross.inhom | Inhomogeneous Cross K Function | |

Lcross.inhom | Inhomogeneous Cross Type L Function | |

Saturated | Saturated Pairwise Interaction model | |

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

Softcore | The Soft Core Point Process Model | |

Smooth | Spatial smoothing of data | |

Penttinen | Penttinen Interaction | |

Pairwise | Generic Pairwise Interaction model | |

WindowOnly | Extract Window of Spatial Object | |

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

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

Smooth.fv | Apply Smoothing to Function Values | |

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

Iest | Estimate the I-function | |

Kinhom | Inhomogeneous K-function | |

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

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

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

MinkowskiSum | Minkowski Sum of Windows | |

Ord | Generic Ord Interaction model | |

LambertW | Lambert's W Function | |

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

Kest | K-function | |

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

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

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

Kmulti | Marked K-Function | |

Strauss | The Strauss Point Process Model | |

Kest.fft | K-function using FFT | |

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

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

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

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

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

angles.psp | Orientation Angles of Line Segments | |

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

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

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

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

Lest | L-function | |

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

Linhom | Inhomogeneous L-function | |

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

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

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

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

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

MultiStrauss | The Multitype Strauss Point Process Model | |

Poisson | Poisson Point Process Model | |

OrdThresh | Ord's Interaction model | |

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

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

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

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

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

affine.tess | Apply Geometrical Transformation To Tessellation | |

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

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

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

add.texture | Fill Plot With Texture | |

append.psp | Combine Two Line Segment Patterns | |

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

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

as.ppp | Convert Data To Class ppp | |

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

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

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

bind.fv | Combine Function Value Tables | |

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

as.ppm | Extract Fitted Point Process Model | |

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

boxx | Multi-Dimensional Box | |

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

blur | Apply Gaussian Blur to a Pixel Image | |

as.psp | Convert Data To Class psp | |

auc | Area Under ROC Curve | |

as.tess | Convert Data To Tessellation | |

Triplets | The Triplet Point Process Model | |

addvar | Added Variable Plot for Point Process Model | |

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

anylist | List of Objects | |

as.im | Convert to Pixel Image | |

Smooth.ssf | Smooth a Spatially Sampled Function | |

affine | Apply Affine Transformation | |

Tstat | Third order summary statistic | |

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

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

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

areaLoss | Difference of Disc Areas | |

branchlabelfun | Tree Branch Membership Labelling Function | |

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

clickdist | Interactively Measure Distance | |

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

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

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

bdist.points | Distance to Boundary of Window | |

area.owin | Area of a Window | |

as.rectangle | Window Frame | |

as.hyperframe | Convert Data to Hyperframe | |

as.fv | Convert Data To Class fv | |

areaGain | Difference of Disc Areas | |

affine.owin | Apply Affine Transformation To Window | |

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

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

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

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

clickbox | Interactively Define a Rectangle | |

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

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

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

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

begins | Check Start of Character String | |

centroid.owin | Centroid of a window | |

as.layered | Convert Data To Layered Object | |

as.interact | Extract Interaction Structure | |

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

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

circdensity | Density Estimation for Circular Data | |

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

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

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

beginner | Print Introduction For Beginners | |

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

border | Border Region of a Window | |

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

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

as.owin | Convert Data To Class owin | |

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

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

bdist.tiles | Distance to Boundary of Window | |

connected | Connected components | |

clickjoin | Interactively join vertices on a plot | |

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

clusterset | Allard-Fraley Estimator of Cluster Feature | |

closepairs | Close Pairs of Points | |

concatxy | Concatenate x,y Coordinate Vectors | |

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

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

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

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

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

bc.ppm | Bias Correction for Fitted Model | |

bdist.pixels | Distance to Boundary of Window | |

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

closing | Morphological Closing | |

clarkevans | Clark and Evans Aggregation Index | |

box3 | Three-Dimensional Box | |

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

covering | Cover Region with Discs | |

bugfixes | List Recent Bug Fixes | |

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

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

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

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

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

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

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

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

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

crossdist | Pairwise distances | |

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

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

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

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

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

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

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

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

boundingcircle | Smallest Enclosing Circle | |

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

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

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

closetriples | Close Triples of Points | |

bw.abram | Abramson's Adaptive Bandwidths | |

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

colourmap | Colour Lookup Tables | |

compatible | Test Whether Objects Are Compatible | |

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

convexhull.xy | Convex Hull of Points | |

clickppp | Interactively Add Points | |

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

convexhull | Convex Hull | |

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

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

complement.owin | Take Complement of a Window | |

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

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

clusterfield | Field of clusters | |

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

dirichletVertices | Vertices and Edges of Dirichlet Tessellation | |

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

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

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

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

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

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

clarkevans.test | Clark and Evans Test | |

dirichletAreas | Compute Areas of Tiles in Dirichlet Tessellation | |

discs | Union of Discs | |

dppBessel | Bessel Type Determinantal Point Process Model | |

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

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

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

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

contour.imlist | Array of Contour Plots | |

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

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

contour.im | Contour plot of pixel image | |

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

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

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

compareFit | Residual Diagnostics for Multiple Fitted Models | |

clusterkernel | Extract Cluster Offspring Kernel | |

clicklpp | Interactively Add Points on a Linear Network | |

distcdf | Distribution Function of Interpoint Distance | |

convexify | Weil's Convexifying Operation | |

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

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

clickpoly | Interactively Define a Polygon | |

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

convolve.im | Convolution of Pixel Images | |

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

dppCauchy | Generalized Cauchy Determinantal Point Process Model | |

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

delaunayDistance | Distance on Delaunay Triangulation | |

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

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

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

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

dilation | Morphological Dilation | |

dppPowerExp | Power Exponential Spectral Determinantal Point Process Model | |

eem | Exponential Energy Marks | |

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

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

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

delaunay | Delaunay Triangulation of Point Pattern | |

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

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

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

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

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

dirichletWeights | Compute Quadrature Weights Based on Dirichlet Tessellation | |

dimhat | Estimate Dimension of Central Subspace | |

dirichlet | Dirichlet Tessellation of Point Pattern | |

deriv.fv | Calculate Derivative of Function Values | |

diameter | Diameter of an Object | |

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

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

distfun | Distance Map as a Function | |

dfbetas.ppm | Parameter Influence Measure | |

colourtools | Convert and Compare Colours in Different Formats | |

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

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

domain | Extract the Domain of any Spatial Object | |

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

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

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

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

disc | Circular Window | |

corners | Corners of a rectangle | |

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

diameter.owin | Diameter of a Window | |

edges | Extract Boundary Edges of a Window. | |

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

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

edit.hyperframe | Invoke Text Editor on Hyperframe | |

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

deltametric | Delta Metric | |

dkernel | Kernel distributions and random generation | |

erosionAny | Morphological Erosion of Windows | |

distfun.lpp | Distance Map on Linear Network | |

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

eval.fv | Evaluate Expression Involving Functions | |

dmixpois | Mixed Poisson Distribution | |

distmap | Distance Map | |

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

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

distmap.ppp | Distance Map of Point Pattern | |

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

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

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

dppapproxkernel | Approximate Determinantal Point Process Kernel | |

edges2triangles | List Triangles in a Graph | |

ellipse | Elliptical Window. | |

discpartarea | Area of Part of Disc | |

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

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

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

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

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

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

eroded.areas | Areas of Morphological Erosions | |

harmonise.owin | Make Windows Compatible | |

emend | Force Model to be Valid | |

fasp.object | Function Arrays for Spatial Patterns | |

ewcdf | Weighted Empirical Cumulative Distribution Function | |

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

incircle | Find Largest Circle Inside Window | |

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

dppeigen | Internal function calculating eig and index | |

edge.Trans | Translation Edge Correction | |

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

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

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

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

erosion | Morphological Erosion by a Disc | |

fardist | Farthest Distance to Boundary of Window | |

foo | Foo is Not a Real Name | |

envelopeArray | Array of Simulation Envelopes of Summary Function | |

imcov | Spatial Covariance of a Pixel Image | |

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

expand.owin | Apply Expansion Rule | |

intensity.ppp | Empirical Intensity of Point Pattern | |

dppMatern | Whittle-Matern Determinantal Point Process Model | |

distmap.owin | Distance Map of Window | |

dppGauss | Gaussian Determinantal Point Process Model | |

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

dilated.areas | Areas of Morphological Dilations | |

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

endpoints.psp | Endpoints of Line Segment Pattern | |

edges2vees | List Dihedral Triples in a Graph | |

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

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

im.object | Class of Images | |

envelope.envelope | Recompute Envelopes | |

fv | Create a Function Value Table | |

harmonise.im | Make Pixel Images Compatible | |

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

dppm | Fit Determinantal Point Process Model | |

insertVertices | Insert New Vertices in a Linear Network | |

gridweights | Compute Quadrature Weights Based on Grid Counts | |

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

flipxy | Exchange X and Y Coordinates | |

gridcentres | Rectangular grid of points | |

eval.im | Evaluate Expression Involving Pixel Images | |

grow.rectangle | Add margins to rectangle | |

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

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

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

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

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

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

funxy | Spatial Function Class | |

fryplot | Fry Plot of Point Pattern | |

linearK | Linear K Function | |

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

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

fourierbasis | Fourier Basis Functions | |

harmonise.msr | Make Measures Compatible | |

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

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

hextess | Hexagonal Grid or Tessellation | |

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

envelope | Simulation Envelopes of Summary Function | |

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

hierpair.family | Hierarchical Pairwise Interaction Process Family | |

harmonic | Basis for Harmonic Functions | |

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

is.marked | Test Whether Marks Are Present | |

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

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

eval.fasp | Evaluate Expression Involving Function Arrays | |

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

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

hyperframe | Hyper Data Frame | |

fv.object | Function Value Table | |

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

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

im | Create a Pixel Image Object | |

hybrid.family | Hybrid Interaction Family | |

harmonise.fv | Make Function Tables Compatible | |

hopskel | Hopkins-Skellam Test | |

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

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

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

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

joinVertices | Join Vertices in a Network | |

inforder.family | Infinite Order Interaction Family | |

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

intersect.tess | Intersection of Two Tessellations | |

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

lineardirichlet | Dirichlet Tessellation on a Linear Network | |

is.rectangle | Determine Type of Window | |

linequad | Quadrature Scheme on a Linear Network | |

lut | Lookup Tables | |

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

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

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

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

increment.fv | Increments of a Function | |

harmonise | Make Objects Compatible | |

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

laslett | Laslett's Transform | |

localK | Neighbourhood density function | |

marks | Marks of a Point Pattern | |

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

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

linearKinhom | Inhomogeneous Linear K Function | |

integral.im | Integral of a Pixel Image | |

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

intensity.quadratcount | Intensity Estimates Using Quadrat Counts | |

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

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

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

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

kernel.factor | Scale factor for density kernel | |

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

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

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

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

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

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

layered | Create List of Plotting Layers | |

lixellate | Subdivide Segments of a Network | |

intensity | Intensity of a Dataset or a Model | |

integral.linim | Integral on a Linear Network | |

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

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

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

integral.msr | Integral of a Measure | |

interp.colourmap | Interpolate smoothly between specified colours | |

infline | Infinite Straight Lines | |

localKinhom | Inhomogeneous Neighbourhood Density Function | |

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

is.multitype | Test whether Object is Multitype | |

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

linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |

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

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

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

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

matchingdist | Distance for a Point Pattern Matching | |

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

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

levelset | Level Set of a Pixel Image | |

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

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

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

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

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

interp.im | Interpolate a Pixel Image | |

localpcf | Local pair correlation function | |

kppm | Fit Cluster or Cox Point Process Model | |

linearpcf | Linear Pair Correlation Function | |

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

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

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

kernel.squint | Integral of Squared Kernel | |

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

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

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

kernel.moment | Moment of Smoothing Kernel | |

linnet | Create a Linear Network | |

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

lengths.psp | Lengths of Line Segments | |

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

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

midpoints.psp | Midpoints of Line Segment Pattern | |

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

lintess | Tessellation on a Linear Network | |

marks.tess | Marks of a Tessellation | |

linim | Create Pixel Image on Linear Network | |

linfun | Function on a Linear Network | |

localKcross.inhom | Inhomogeneous Multitype K Function | |

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

lohboot | Bootstrap Confidence Bands for Summary Function | |

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

nnmap | K-th Nearest Point Map | |

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

msr | Signed or Vector-Valued Measure | |

measureContinuous | Discrete and Continuous Components of a Measure | |

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

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

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

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

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

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

lurking | Lurking Variable Plot | |

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

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

nndist | Nearest neighbour distances | |

methods.linnet | Methods for Linear Networks | |

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

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

markconnect | Mark Connection Function | |

nnmark | Mark of Nearest Neighbour | |

markcorr | Mark Correlation Function | |

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

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

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

lpp | Create Point Pattern on Linear Network | |

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

mergeLevels | Merge Levels of a Factor | |

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

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

miplot | Morisita Index Plot | |

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

methods.layered | Methods for Layered Objects | |

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

periodify | Make Periodic Copies of a Spatial Pattern | |

methods.ssf | Methods for Spatially Sampled Functions | |

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

logLik.slrm | Loglikelihood of Spatial Logistic Regression | |

markvario | Mark Variogram | |

pairdist.ppp | Pairwise distances | |

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

overlap.owin | Compute Area of Overlap | |

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

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

methods.distfun | Geometrical Operations for Distance Functions | |

mincontrast | Method of Minimum Contrast | |

markcrosscorr | Mark Cross-Correlation Function | |

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

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

methods.zclustermodel | Methods for Cluster Models | |

methods.unitname | Methods for Units | |

nestsplit | Nested Split | |

pairdist.ppx | Pairwise Distances in Any Dimensions | |

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

markmarkscatter | Mark-Mark Scatter Plot | |

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

nnclean | Nearest Neighbour Clutter Removal | |

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

parameters | Extract Model Parameters in Understandable Form | |

parres | Partial Residuals for Point Process Model | |

nncross | Nearest Neighbours Between Two Patterns | |

matrixpower | Power of a Matrix | |

nearestsegment | Find Line Segment Nearest to Each Point | |

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

nvertices | Count Number of Vertices | |

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

npfun | Dummy Function Returns Number of Points | |

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

nnwhich.ppx | Nearest Neighbours in Any Dimensions | |

npoints | Number of Points in a Point Pattern | |

plot.cdftest | Plot a Spatial Distribution Test | |

pairdist.psp | Pairwise distances between line segments | |

persp.im | Perspective Plot of Pixel Image | |

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

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

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

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

methods.objsurf | Methods for Objective Function Surfaces | |

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

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

methods.fii | Methods for Fitted Interactions | |

methods.funxy | Methods for Spatial Functions | |

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

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

plot.hyperframe | Plot Entries in a Hyperframe | |

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

plot.bermantest | Plot Result of Berman Test | |

owin | Create a Window | |

multiplicity.ppp | Count Multiplicity of Duplicate Points | |

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

pairs.im | Scatterplot Matrix for Pixel Images | |

plot.owin | Plot a Spatial Window | |

nnorient | Nearest Neighbour Orientation Distribution | |

owin.object | Class owin | |

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

pairwise.family | Pairwise Interaction Process Family | |

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

pairorient | Point Pair Orientation Distribution | |

plot.fv | Plot Function Values | |

ord.family | Ord Interaction Process Family | |

plot.im | Plot a Pixel Image | |

nnwhich.pp3 | Nearest neighbours in three dimensions | |

padimage | Pad the Border of a Pixel Image | |

pixellate | Convert Spatial Object to Pixel Image | |

pool.envelope | Pool Data from Several Envelopes | |

psp.object | Class of Line Segment Patterns | |

pairdist | Pairwise distances | |

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

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

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

nnwhich | Nearest neighbour | |

print.quad | Print a Quadrature Scheme | |

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

plot.texturemap | Plot a Texture Map | |

pixellate.owin | Convert Window to Pixel Image | |

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

plot.leverage.ppm | Plot Leverage Function | |

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

plot.imlist | Plot a List of Images | |

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

plot.layered | Layered Plot | |

points.lpp | Draw Points on Existing Plot | |

plot.colourmap | Plot a Colour Map | |

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

nnfun | Nearest Neighbour Index Map as a Function | |

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

pairdist.default | Pairwise distances | |

objsurf | Objective Function Surface | |

pcfmulti | Marked pair correlation function | |

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

model.images | Compute Images of Constructed Covariates | |

pcf | Pair Correlation Function | |

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

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

opening | Morphological Opening | |

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

plot.quadratcount | Plot Quadrat Counts | |

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

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

plot.laslett | Plot Laslett Transform | |

pointsOnLines | Place Points Evenly Along Specified Lines | |

ppm | Fit Point Process Model to Data | |

plot.envelope | Plot a Simulation Envelope | |

ppp.object | Class of Point Patterns | |

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

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

plot.influence.ppm | Plot Influence Measure | |

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

pixelcentres | Extract Pixel Centres as Point Pattern | |

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

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

ppp | Create a Point Pattern | |

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

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

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

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

rPenttinen | Perfect Simulation of the Penttinen Process | |

quasirandom | Quasirandom Patterns | |

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

rNeymanScott | Simulate Neyman-Scott Process | |

pixelquad | Quadrature Scheme Based on Pixel Grid | |

plot.linnet | Plot a linear network | |

plot.anylist | Plot a List of Things | |

pp3 | Three Dimensional Point Pattern | |

plot.textstring | Plot a Text String | |

perimeter | Perimeter Length of Window | |

plot.fasp | Plot a Function Array | |

psib | Sibling Probability of Cluster Point Process | |

pairdist.pp3 | Pairwise distances in Three Dimensions | |

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

plot.studpermutest | Plot a Studentised Permutation Test | |

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

regularpolygon | Create A Regular Polygon | |

quadratcount | Quadrat counting for a point pattern | |

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

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

plot.quad | Plot a Spatial Quadrature Scheme | |

rcelllpp | Simulate Cell Process on Linear Network | |

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

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

plot.ppp | plot a Spatial Point Pattern | |

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

quad.object | Class of Quadrature Schemes | |

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

plot.symbolmap | Plot a Graphics Symbol Map | |

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

plot.pppmatching | Plot a Point Matching | |

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

pool | Pool Data | |

plot.ssf | Plot a Spatially Sampled Function | |

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

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

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

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

plot.tess | Plot a Tessellation | |

rGaussPoisson | Simulate Gauss-Poisson Process | |

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

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

pppmatching | Create a Point Matching | |

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

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

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

progressreport | Print Progress Reports | |

pcfinhom | Inhomogeneous Pair Correlation Function | |

polynom | Polynomial in One or Two Variables | |

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

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

rdpp | Simulation of a Determinantal Point Process | |

pppmatching.object | Class of Point Matchings | |

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

quadrats | Divide Region into Quadrats | |

ppx | Multidimensional Space-Time Point Pattern | |

rMatClust | Simulate Matern Cluster Process | |

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

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

plot.listof | Plot a List of Things | |

plot.onearrow | Plot an Arrow | |

project2segment | Move Point To Nearest Line | |

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

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

psp | Create a Line Segment Pattern | |

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

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

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

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

rSSI | Simulate Simple Sequential Inhibition | |

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

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

rMaternI | Simulate Matern Model I | |

rotate.infline | Rotate or Shift Infinite Lines | |

rmpoispp | Generate Multitype Poisson Point Pattern | |

ripras | Estimate window from points alone | |

quadratresample | Resample a Point Pattern by Resampling Quadrats | |

rectdistmap | Distance Map Using Rectangular Distance Metric | |

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

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

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

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

rLGCP | Simulate Log-Gaussian Cox Process | |

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

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

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

polartess | Tessellation Using Polar Coordinates | |

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

rmhexpand | Specify Simulation Window or Expansion Rule | |

project2set | Find Nearest Point in a Region | |

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

rStrauss | Perfect Simulation of the Strauss Process | |

rectcontact | Contact Distribution Function using Rectangular Structuring Element | |

rMaternII | Simulate Matern Model II | |

quantile.im | Sample Quantiles of Pixel Image | |

rshift.ppp | Randomly Shift a Point Pattern | |

rotate.owin | Rotate a Window | |

rThomas | Simulate Thomas Process | |

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

rnoise | Random Pixel Noise | |

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

reach | Interaction Distance of a Point Process | |

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

rjitter | Random Perturbation of a Point Pattern | |

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

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

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

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

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

rshift | Random Shift | |

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

rstrat | Simulate Stratified Random Point Pattern | |

rsyst | Simulate systematic random point pattern | |

rounding | Detect Numerical Rounding | |

rlpp | Random Points on a Linear Network | |

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

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

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

rknn | Theoretical Distribution of Nearest Neighbour Distance | |

rppm | Recursively Partitioned Point Process Model | |

rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |

rpoint | Generate N Random Points | |

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

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

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

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

rthin | Random Thinning | |

scaletointerval | Rescale Data to Lie Between Specified Limits | |

pool.fv | Pool Several Functions | |

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

sdrPredict | Compute Predictors from Sufficient Dimension Reduction | |

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

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

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

pool.quadrattest | Pool Several Quadrat Tests | |

pppdist | Distance Between Two Point Patterns | |

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

shift.owin | Apply Vector Translation To Window | |

sharpen | Data Sharpening of Point Pattern | |

setcov | Set Covariance of a Window | |

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

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

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

scalardilate | Apply Scalar Dilation | |

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

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

rags | Alternating Gibbs Sampler for Multitype Point Processes | |

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

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

quantess | Quantile Tessellation | |

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

rex | Richardson Extrapolation | |

rHardcore | Perfect Simulation of the Hardcore Process | |

relrisk | Estimate of Spatially-Varying Relative Risk | |

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

quantile.density | Quantiles of a Density Estimate | |

rat | Ratio object | |

rcell | Simulate Baddeley-Silverman Cell Process | |

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

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

roc | Receiver Operating Characteristic | |

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

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

rotate.ppp | Rotate a Point Pattern | |

rotate.psp | Rotate a Line Segment Pattern | |

rpoisline | Generate Poisson Random Line Process | |

rose | Rose Diagram | |

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

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

rotmean | Rotational Average of a Pixel Image | |

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

rescale | Convert dataset to another unit of length | |

rpoislinetess | Poisson Line Tessellation | |

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

rescue.rectangle | Convert Window Back To Rectangle | |

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

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

requireversion | Require a Specific Version of a Package | |

rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |

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

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

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

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

rthinclumps | Random Thinning of Clumps | |

rMosaicField | Mosaic Random Field | |

repairNetwork | Repair Internal Data in a Linear Network | |

range.fv | Range of Function Values | |

rpoispp | Generate Poisson Point Pattern | |

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

rPoissonCluster | Simulate Poisson Cluster Process | |

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

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

rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |

sdr | Sufficient Dimension Reduction | |

scanpp | Read Point Pattern From Data File | |

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

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

shift | Apply Vector Translation | |

scanLRTS | Likelihood Ratio Test Statistic for Scan Test | |

scan.test | Spatial Scan Test | |

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

rQuasi | Generate Quasirandom Point Pattern in Given Window | |

rMosaicSet | Mosaic Random Set | |

rlabel | Random Re-Labelling of Point Pattern | |

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

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

reflect | Reflect In Origin | |

rgbim | Create Colour-Valued Pixel Image | |

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

rmpoint | Generate N Random Multitype Points | |

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

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

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

rotate.im | Rotate a Pixel Image | |

rpoislpp | Poisson Point Process on a Linear Network | |

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

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

rotate | Rotate | |

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

runifpoint | Generate N Uniform Random Points | |

runiflpp | Uniform Random Points on a Linear Network | |

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

No Results! |

## Vignettes of spatstat

Name | ||

bugfixes.Rnw | ||

datasets.Rnw | ||

getstart.Rnw | ||

hexagon.eps | ||

hexagon.pdf | ||

irregpoly.eps | ||

irregpoly.pdf | ||

replicated.Rnw | ||

shapefiles.Rnw | ||

updates.Rnw | ||

No Results! |

## Last month downloads

## Details

Date | 2019-12-10 |

License | GPL (>= 2) |

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

LazyData | true |

NeedsCompilation | yes |

ByteCompile | true |

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

Packaged | 2019-12-10 05:34:15 UTC; adrian |

Repository | CRAN |

Date/Publication | 2019-12-10 11:50:03 UTC |

imports | abind , deldir (>= 0.0-21) , goftest (>= 1.2-2) , Matrix , mgcv , polyclip (>= 1.10-0) , spatstat.utils (>= 1.15-0) , 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 , spatstat.data (>= 1.4-0) , stats , utils |

Contributors | Adrian Baddeley, Rolf Turner, Ege Rubak |

#### Include our badge in your README

```
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```