# spatstat v1.56-1

Monthly downloads

## 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 | |

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

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

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

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

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

FmultiInhom | Inhomogeneous Marked F-Function | |

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

Finhom | Inhomogeneous Empty Space Function | |

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

Extract.quad | Subset of Quadrature Scheme | |

Ginhom | Inhomogeneous Nearest Neighbour Function | |

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

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

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

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

Extract.linnet | Extract Subset of Linear Network | |

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

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

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

[.ssf | Subset of spatially sampled function | |

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

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

Gcom | Model Compensator of Nearest Neighbour Function | |

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

Gmulti | Marked Nearest Neighbour Distance Function | |

Extract.im | Extract Subset of Image | |

Iest | Estimate the I-function | |

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

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

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

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

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

Fiksel | The Fiksel Interaction | |

Hardcore | The Hard Core Point Process Model | |

Kinhom | Inhomogeneous K-function | |

Extract.owin | Extract Subset of Window | |

Gest | Nearest Neighbour Distance Function G | |

GmultiInhom | Inhomogeneous Marked G-Function | |

Kmark | Mark-Weighted K Function | |

Gres | Residual G Function | |

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

Hest | Spherical Contact Distribution Function | |

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

Kmeasure | Reduced Second Moment Measure | |

Hybrid | Hybrid Interaction Point Process Model | |

Kcom | Model Compensator of K Function | |

LambertW | Lambert's W Function | |

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

Geyer | Geyer's Saturation Point Process Model | |

HierHard | The Hierarchical Hard Core Point Process Model | |

Kmulti.inhom | Inhomogeneous Marked K-Function | |

Gfox | Foxall's Distance Functions | |

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

Ldot.inhom | Inhomogeneous Multitype L Dot Function | |

Jinhom | Inhomogeneous J-function | |

Pairwise | Generic Pairwise Interaction model | |

Kres | Residual K Function | |

Penttinen | Penttinen Interaction | |

Jmulti | Marked J Function | |

HierStrauss | The Hierarchical Strauss Point Process Model | |

Kest | K-function | |

Kest.fft | K-function using FFT | |

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

Kscaled | Locally Scaled K-function | |

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

Kcross.inhom | Inhomogeneous Cross K Function | |

LennardJones | The Lennard-Jones Potential | |

Ord | Generic Ord Interaction model | |

Jest | Estimate the J-function | |

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

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

OrdThresh | Ord's Interaction model | |

Kmulti | Marked K-Function | |

Lest | L-function | |

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

Lcross.inhom | Inhomogeneous Cross Type L Function | |

Poisson | Poisson Point Process Model | |

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

Smooth | Spatial smoothing of data | |

Saturated | Saturated Pairwise Interaction model | |

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

Linhom | L-function | |

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

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

Ksector | Sector K-function | |

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

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

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

Strauss | The Strauss Point Process Model | |

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

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

Kdot.inhom | Inhomogeneous Multitype K Dot Function | |

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

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

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

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

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

MinkowskiSum | Minkowski Sum of Windows | |

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

Ops.msr | Arithmetic Operations on Measures | |

Smooth.fv | Apply Smoothing to Function Values | |

WindowOnly | Extract Window of Spatial Object | |

MultiStrauss | The Multitype Strauss Point Process Model | |

MultiHard | The Multitype Hard Core Point Process Model | |

affine.tess | Apply Geometrical Transformation To Tessellation | |

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

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

addvar | Added Variable Plot for Point Process Model | |

Softcore | The Soft Core Point Process Model | |

add.texture | Fill Plot With Texture | |

Triplets | The Triplet Point Process Model | |

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

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

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

affine | Apply Affine Transformation | |

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

angles.psp | Orientation Angles of Line Segments | |

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

anylist | List of Objects | |

Tstat | Third order summary statistic | |

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

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

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

Smooth.ssf | Smooth a Spatially Sampled Function | |

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

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

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

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

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

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

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

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

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

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

affine.owin | Apply Affine Transformation To Window | |

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

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

as.layered | Convert Data To Layered Object | |

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

as.im | Convert to Pixel Image | |

as.interact | Extract Interaction Structure | |

auc | Area Under ROC Curve | |

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

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

as.ppm | Extract Fitted Point Process Model | |

areaLoss | Difference of Disc Areas | |

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

as.ppp | Convert Data To Class ppp | |

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

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

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

bind.fv | Combine Function Value Tables | |

append.psp | Combine Two Line Segment Patterns | |

as.hyperframe | Convert Data to Hyperframe | |

border | Border Region of a Window | |

bc.ppm | Bias Correction for Fitted Model | |

branchlabelfun | Tree Branch Membership Labelling Function | |

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

box3 | Three-Dimensional Box | |

bugfixes | List Recent Bug Fixes | |

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

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

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

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

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

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

boxx | Multi-Dimensional Box | |

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

centroid.owin | Centroid of a window | |

clickpoly | Interactively Define a Polygon | |

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

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

clickppp | Interactively Add Points | |

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

contour.imlist | Array of Contour Plots | |

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

circdensity | Density Estimation for Circular Data | |

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

area.owin | Area of a Window | |

convexhull | Convex Hull | |

as.owin | Convert Data To Class owin | |

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

areaGain | Difference of Disc Areas | |

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

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

closetriples | Close Triples of Points | |

convexhull.xy | Convex Hull of Points | |

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

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

as.fv | Convert Data To Class fv | |

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

clusterset | Allard-Fraley Estimator of Cluster Feature | |

convexify | Weil's Convexifying Operation | |

colourmap | Colour Lookup Tables | |

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

compareFit | Residual Diagnostics for Multiple Fitted Models | |

compatible | Test Whether Objects Are Compatible | |

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

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

complement.owin | Take Complement of a Window | |

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

crossdist | Pairwise distances | |

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

concatxy | Concatenate x,y Coordinate Vectors | |

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

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

as.psp | Convert Data To Class psp | |

convolve.im | Convolution of Pixel Images | |

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

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

bdist.pixels | Distance to Boundary of Window | |

as.rectangle | Window Frame | |

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

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

bdist.points | Distance to Boundary of Window | |

bdist.tiles | Distance to Boundary of Window | |

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

blur | Apply Gaussian Blur to a Pixel Image | |

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

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

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

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

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

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

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

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

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

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

clarkevans | Clark and Evans Aggregation Index | |

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

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

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

delaunay | Delaunay Triangulation of Point Pattern | |

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

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

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

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

diameter.owin | Diameter of a Window | |

dilated.areas | Areas of Morphological Dilations | |

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

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

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

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

as.tess | Convert Data To Tessellation | |

beginner | Print Introduction For Beginners | |

clarkevans.test | Clark and Evans Test | |

closepairs | Close Pairs of Points | |

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

clickbox | Interactively Define a Rectangle | |

closing | Morphological Closing | |

distfun | Distance Map as a Function | |

clusterfield | Field of clusters | |

dilation | Morphological Dilation | |

diameter | Diameter of an Object | |

begins | Check Start of Character String | |

clickdist | Interactively Measure Distance | |

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

distfun.lpp | Distance Map on Linear Network | |

dppGauss | Gaussian Determinantal Point Process Model | |

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

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

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

dppMatern | Whittle-Matern Determinantal Point Process Model | |

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

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

boundingcircle | Smallest Enclosing Circle | |

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

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

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

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

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

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

distmap | Distance Map | |

colourtools | Convert and Compare Colours in Different Formats | |

dirichletWeights | Compute Quadrature Weights Based on Dirichlet Tessellation | |

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

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

distmap.owin | Distance Map of Window | |

dmixpois | Mixed Poisson Distribution | |

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

disc | Circular Window | |

ellipse | Elliptical Window. | |

discs | Union of Discs | |

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

distcdf | Distribution Function of Interpoint Distance | |

domain | Extract the Domain of any Spatial Object | |

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

envelope.envelope | Recompute Envelopes | |

discpartarea | Area of Part of Disc | |

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

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

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

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

edges2vees | List Dihedral Triples in a Graph | |

fardist | Farthest Distance to Boundary of Window | |

connected | Connected components | |

edit.hyperframe | Invoke Text Editor on Hyperframe | |

fasp.object | Function Arrays for Spatial Patterns | |

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

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

corners | Corners of a rectangle | |

dppPowerExp | Power Exponential Spectral Determinantal Point Process Model | |

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

covering | Cover Region with Discs | |

edge.Trans | Translation Edge Correction | |

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

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

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

clickjoin | Interactively join vertices on a plot | |

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

erosionAny | Morphological Erosion of Windows | |

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

fv.object | Function Value Table | |

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

clicklpp | Interactively Add Points on a Linear Network | |

clusterkernel | Extract Cluster Offspring Kernel | |

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

eem | Exponential Energy Marks | |

eval.fasp | Evaluate Expression Involving Function Arrays | |

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

dppapproxkernel | Approximate Determinantal Point Process Kernel | |

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

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

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

ewcdf | Weighted Empirical Cumulative Distribution Function | |

gridcentres | Rectangular grid of points | |

deltametric | Delta Metric | |

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

dfbetas.ppm | Parameter Influence Measure | |

fourierbasis | Fourier Basis Functions | |

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

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

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

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

fryplot | Fry Plot of Point Pattern | |

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

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

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

contour.im | Contour plot of pixel image | |

endpoints.psp | Endpoints of Line Segment Pattern | |

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

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

harmonise.owin | Make Windows Compatible | |

im | Create a Pixel Image Object | |

dirichletAreas | Compute Areas of Tiles in Dirichlet Tessellation | |

envelope | Simulation Envelopes of Summary Function | |

harmonise.im | Make Pixel Images Compatible | |

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

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

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

dirichletVertices | Vertices and Edges of Dirichlet Tessellation | |

distmap.ppp | Distance Map of Point Pattern | |

harmonise.msr | Make Measures Compatible | |

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

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

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

im.object | Class of Images | |

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

dppBessel | Bessel Type Determinantal Point Process Model | |

dppCauchy | Generalized Cauchy Determinantal Point Process Model | |

incircle | Find Largest Circle Inside Window | |

grow.rectangle | Add margins to rectangle | |

increment.fv | Increments of a Function | |

harmonic | Basis for Harmonic Functions | |

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

infline | Infinite Straight Lines | |

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

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

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

intensity.ppp | Empirical Intensity of Point Pattern | |

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

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

hextess | Hexagonal Grid or Tessellation | |

integral.im | Integral of a Pixel Image | |

dppm | Fit Determinantal Point Process Model | |

integral.linim | Integral on a Linear Network | |

interp.im | Interpolate a Pixel Image | |

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

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

hopskel | Hopkins-Skellam Test | |

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

is.marked | Test Whether Marks Are Present | |

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

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

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

delaunayDistance | Distance on Delaunay Triangulation | |

edges | Extract Boundary Edges of a Window. | |

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

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

edges2triangles | List Triangles in a Graph | |

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

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

eroded.areas | Areas of Morphological Erosions | |

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

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

erosion | Morphological Erosion by a Disc | |

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

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

linim | Create Pixel Image on Linear Network | |

deriv.fv | Calculate Derivative of Function Values | |

is.rectangle | Determine Type of Window | |

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

eval.fv | Evaluate Expression Involving Functions | |

msr | Signed or Vector-Valued Measure | |

eval.im | Evaluate Expression Involving Pixel Images | |

intensity.quadratcount | Intensity Estimates Using Quadrat Counts | |

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

interp.colourmap | Interpolate smoothly between specified colours | |

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

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

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

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

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

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

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

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

laslett | Laslett's Transform | |

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

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

levelset | Level Set of a Pixel Image | |

gridweights | Compute Quadrature Weights Based on Grid Counts | |

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

kernel.moment | Moment of Smoothing Kernel | |

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

hierpair.family | Hierarchical Pairwise Interaction Process Family | |

joinVertices | Join Vertices in a Network | |

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

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

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

hybrid.family | Hybrid Interaction Family | |

linfun | Function on a Linear Network | |

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

layered | Create List of Plotting Layers | |

kernel.squint | Integral of Squared Kernel | |

dimhat | Estimate Dimension of Central Subspace | |

is.multitype | Test whether Object is Multitype | |

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

lohboot | Bootstrap Confidence Bands for Summary Function | |

dirichlet | Dirichlet Tessellation of Point Pattern | |

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

hyperframe | Hyper Data Frame | |

dkernel | Kernel distributions and random generation | |

kernel.factor | Scale factor for density kernel | |

lengths.psp | Lengths of Line Segments | |

localpcf | Local pair correlation function | |

imcov | Spatial Covariance of a Pixel Image | |

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

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

lineardirichlet | Dirichlet Tessellation on a Linear Network | |

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

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

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

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

linnet | Create a Linear Network | |

dppeigen | Internal function calculating eig and index | |

ord.family | Ord Interaction Process Family | |

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

linearpcf | Linear Pair Correlation Function | |

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

linearKinhom | Inhomogeneous Linear K Function | |

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

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

lpp | Create Point Pattern on Linear Network | |

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

linearK | Linear K Function | |

matchingdist | Distance for a Point Pattern Matching | |

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

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

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

lut | Lookup Tables | |

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

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

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

markvario | Mark Variogram | |

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

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

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

methods.ssf | Methods for Spatially Sampled Functions | |

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

linequad | Quadrature Scheme on a Linear Network | |

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

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

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

emend | Force Model to be Valid | |

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

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

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

methods.unitname | Methods for Units | |

kppm | Fit Cluster or Cox Point Process Model | |

methods.fii | Methods for Fitted Interactions | |

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

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

nnmap | K-th Nearest Point Map | |

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

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

measureContinuous | Discrete and Continuous Components of a Measure | |

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

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

envelopeArray | Array of Simulation Envelopes of Summary Function | |

lixellate | Subdivide Segments of a Network | |

pixelquad | Quadrature Scheme Based on Pixel Grid | |

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

nndist | Nearest neighbour distances | |

model.images | Compute Images of Constructed Covariates | |

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

expand.owin | Apply Expansion Rule | |

flipxy | Exchange X and Y Coordinates | |

nnorient | Nearest Neighbour Orientation Distribution | |

lurking | Lurking Variable Plot | |

foo | Foo is Not a Real Name | |

markconnect | Mark Connection Function | |

methods.layered | Methods for Layered Objects | |

funxy | Spatial Function Class | |

fv | Create a Function Value Table | |

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

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

pcfmulti | Marked pair correlation function | |

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

nnfun | Nearest Neighbour Index Map as a Function | |

pairwise.family | Pairwise Interaction Process Family | |

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

midpoints.psp | Midpoints of Line Segment Pattern | |

nnmark | Mark of Nearest Neighbour | |

overlap.owin | Compute Area of Overlap | |

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

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

nestsplit | Nested Split | |

markcorr | Mark Correlation Function | |

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

harmonise | Make Objects Compatible | |

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

pairdist.default | Pairwise distances | |

lintess | Tessellation on a Linear Network | |

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

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

pairdist.psp | Pairwise distances between line segments | |

linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |

harmonise.fv | Make Function Tables Compatible | |

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

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

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

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

nnclean | Nearest Neighbour Clutter Removal | |

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

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

methods.zclustermodel | Methods for Cluster Models | |

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

plot.colourmap | Plot a Colour Map | |

miplot | Morisita Index Plot | |

inforder.family | Infinite Order Interaction Family | |

marks.tess | Marks of a Tessellation | |

insertVertices | Insert New Vertices in a Linear Network | |

integral.msr | Integral of a Measure | |

mincontrast | Method of Minimum Contrast | |

npoints | Number of Points in a Point Pattern | |

matrixpower | Power of a Matrix | |

nearestsegment | Find Line Segment Nearest to Each Point | |

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

methods.linnet | Methods for Linear Networks | |

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

perimeter | Perimeter Length of Window | |

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

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

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

nncross | Nearest Neighbours Between Two Patterns | |

pixelcentres | Extract Pixel Centres as Point Pattern | |

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

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

intensity | Intensity of a Dataset or a Model | |

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

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

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

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

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

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

nnwhich.ppx | Nearest Neighbours in Any Dimensions | |

mergeLevels | Merge Levels of a Factor | |

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

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

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

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

plot.texturemap | Plot a Texture Map | |

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

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

plot.leverage.ppm | Plot Leverage Function | |

nvertices | Count Number of Vertices | |

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

plot.bermantest | Plot Result of Berman Test | |

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

intersect.tess | Intersection of Two Tessellations | |

parameters | Extract Model Parameters in Understandable Form | |

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

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

pcfinhom | Inhomogeneous Pair Correlation Function | |

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

rMosaicField | Mosaic Random Field | |

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

methods.distfun | Geometrical Operations for Distance Functions | |

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

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

quadratresample | Resample a Point Pattern by Resampling Quadrats | |

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

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

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

nnwhich | Nearest neighbour | |

pool | Pool Data | |

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

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

plot.anylist | Plot a List of Things | |

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

parres | Partial Residuals for Point Process Model | |

pairdist.ppx | Pairwise Distances in Any Dimensions | |

pairdist.ppp | Pairwise distances | |

pairorient | Point Pair Orientation Distribution | |

plot.im | Plot a Pixel Image | |

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

quantile.density | Quantiles of a Density Estimate | |

npfun | Dummy Function Returns Number of Points | |

pairs.im | Scatterplot Matrix for Pixel Images | |

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

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

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

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

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

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

plot.ppp | plot a Spatial Point Pattern | |

project2set | Find Nearest Point in a Region | |

plot.fv | Plot Function Values | |

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

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

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

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

plot.hyperframe | Plot Entries in a Hyperframe | |

plot.layered | Layered Plot | |

quantess | Quantile Tessellation | |

quadratcount | Quadrat counting for a point pattern | |

nnwhich.pp3 | Nearest neighbours in three dimensions | |

plot.cdftest | Plot a Spatial Distribution Test | |

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

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

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

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

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

plot.symbolmap | Plot a Graphics Symbol Map | |

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

logLik.slrm | Loglikelihood of Spatial Logistic Regression | |

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

pixellate | Convert Spatial Object to Pixel Image | |

ppm | Fit Point Process Model to Data | |

rMosaicSet | Mosaic Random Set | |

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

localK | Neighbourhood density function | |

owin | Create a Window | |

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

localKinhom | Inhomogeneous Neighbourhood Density Function | |

owin.object | Class owin | |

pixellate.owin | Convert Window to Pixel Image | |

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

plot.imlist | Plot a List of Images | |

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

print.quad | Print a Quadrature Scheme | |

plot.laslett | Plot Laslett Transform | |

rags | Alternating Gibbs Sampler for Multitype Point Processes | |

project2segment | Move Point To Nearest Line | |

plot.listof | Plot a List of Things | |

plot.onearrow | Plot an Arrow | |

plot.textstring | Plot a Text String | |

pairdist.pp3 | Pairwise distances in Three Dimensions | |

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

pointsOnLines | Place Points Evenly Along Specified Lines | |

rescale | Convert dataset to another unit of length | |

polynom | Polynomial in One or Two Variables | |

rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |

rectcontact | Contact Distribution Function using Rectangular Structuring Element | |

points.lpp | Draw Points on Existing Plot | |

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

ppp | Create a Point Pattern | |

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

persp.im | Perspective Plot of Pixel Image | |

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

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

plot.tess | Plot a Tessellation | |

marks | Marks of a Point Pattern | |

plot.influence.ppm | Plot Influence Measure | |

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

plot.owin | Plot a Spatial Window | |

markcrosscorr | Mark Cross-Correlation Function | |

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

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

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

plot.linnet | Plot a linear network | |

rotate.im | Rotate a Pixel Image | |

ripras | Estimate window from points alone | |

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

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

plot.studpermutest | Plot a Studentised Permutation Test | |

methods.funxy | Methods for Spatial Functions | |

ppp.object | Class of Point Patterns | |

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

methods.objsurf | Methods for Objective Function Surfaces | |

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

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

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

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

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

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

range.fv | Range of Function Values | |

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

psib | Sibling Probability of Cluster Point Process | |

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

rjitter | Random Perturbation of a Point Pattern | |

plot.ssf | Plot a Spatially Sampled Function | |

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

multiplicity.ppp | Count Multiplicity of Duplicate Points | |

reflect | Reflect In Origin | |

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

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

rGaussPoisson | Simulate Gauss-Poisson Process | |

rStrauss | Perfect Simulation of the Strauss Process | |

pppmatching.object | Class of Point Matchings | |

quantile.im | Sample Quantiles of Pixel Image | |

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

rlpp | Random Points on a Linear Network | |

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

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

ppx | Multidimensional Space-Time Point Pattern | |

rPoissonCluster | Simulate Poisson Cluster Process | |

reach | Interaction Distance of a Point Process | |

rLGCP | Simulate Log-Gaussian Cox Process | |

padimage | Pad the Border of a Pixel Image | |

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

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

pp3 | Three Dimensional Point Pattern | |

rHardcore | Perfect Simulation of the Hardcore Process | |

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

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

rotmean | Rotational Average of a Pixel Image | |

periodify | Make Periodic Copies of a Spatial Pattern | |

rPenttinen | Perfect Simulation of the Penttinen Process | |

progressreport | Print Progress Reports | |

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

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

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

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

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

quadrats | Divide Region into Quadrats | |

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

rmhexpand | Specify Simulation Window or Expansion Rule | |

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

objsurf | Objective Function Surface | |

rNeymanScott | Simulate Neyman-Scott Process | |

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

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

rthin | Random Thinning | |

pool.envelope | Pool Data from Several Envelopes | |

opening | Morphological Opening | |

rpoispp | Generate Poisson Point Pattern | |

pairdist | Pairwise distances | |

rotate.infline | Rotate or Shift Infinite Lines | |

rescue.rectangle | Convert Window Back To Rectangle | |

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

pcf | Pair Correlation Function | |

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

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

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

sharpen | Data Sharpening of Point Pattern | |

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

rMaternII | Simulate Matern Model II | |

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

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

tileindex | Determine Which Tile Contains Each Given Point | |

rMaternI | Simulate Matern Model I | |

rQuasi | Generate Quasirandom Point Pattern in Given Window | |

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

plot.envelope | Plot a Simulation Envelope | |

plot.fasp | Plot a Function Array | |

rdpp | Simulation of a Determinantal Point Process | |

rMatClust | Simulate Matern Cluster Process | |

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

rshift.ppp | Randomly Shift a Point Pattern | |

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

rSSI | Simulate Simple Sequential Inhibition | |

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

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

rcell | Simulate Baddeley-Silverman Cell Process | |

rat | Ratio object | |

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

studpermu.test | Studentised Permutation Test | |

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

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

plot.quad | Plot a Spatial Quadrature Scheme | |

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

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

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

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

rknn | Theoretical Distribution of Nearest Neighbour Distance | |

regularpolygon | Create A Regular Polygon | |

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

rounding | Detect Numerical Rounding | |

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

relrisk | Estimate of Spatially-Varying Relative Risk | |

requireversion | Require a Specific Version of a Package | |

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

spatstat.options | Internal Options in Spatstat Package | |

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

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

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

rpoislinetess | Poisson Line Tessellation | |

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

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

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

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

subfits | Extract List of Individual Point Process Models | |

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

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

rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |

rlabel | Random Re-Labelling of Point Pattern | |

setcov | Set Covariance of a Window | |

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

rotate | Rotate | |

plot.quadratcount | Plot Quadrat Counts | |

rpoislpp | Poisson Point Process on a Linear Network | |

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

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

pppdist | Distance Between Two Point Patterns | |

rshift | Random Shift | |

rmpoispp | Generate Multitype Poisson Point Pattern | |

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

rppm | Recursively Partitioned Point Process Model | |

rotate.psp | Rotate a Line Segment Pattern | |

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

test.crossing.psp | Check Whether Segments Cross | |

rnoise | Random Pixel Noise | |

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

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

valid | Check Whether Point Process Model is Valid | |

rpoint | Generate N Random Points | |

rmpoint | Generate N Random Multitype Points | |

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

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

pool.fv | Pool Several Functions | |

spatdim | Spatial Dimension of a Dataset | |

scalardilate | Apply Scalar Dilation | |

runifpoint | Generate N Uniform Random Points | |

rose | Rose Diagram | |

rpoisline | Generate Poisson Random Line Process | |

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

scaletointerval | Rescale Data to Lie Between Specified Limits | |

shift | Apply Vector Translation | |

roc | Receiver Operating Characteristic | |

solist | List of Two-Dimensional Spatial Objects | |

spatstat-deprecated | Deprecated spatstat functions | |

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

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

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

pool.quadrattest | Pool Several Quadrat Tests | |

sdrPredict | Compute Predictors from Sufficient Dimension Reduction | |

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

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

spatialcdf | Spatial Cumulative Distribution Function | |

rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |

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

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

spatstat-internal | Internal spatstat functions | |

spatstat-package | The Spatstat Package | |

rsyst | Simulate systematic random point pattern | |

unmark | Remove Marks | |

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

triangulate.owin | Decompose Window into Triangles | |

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

psp | Create a Line Segment Pattern | |

ssf | Spatially Sampled Function | |

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

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

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

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

summary.owin | Summary of a Spatial Window | |

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

stieltjes | Compute Integral of Function Against Cumulative Distribution | |

triplet.family | Triplet Interaction Family | |

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

summary.im | Summarizing a Pixel Image | |

spokes | Spokes pattern of dummy points | |

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

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

pppmatching | Create a Point Matching | |

tess | Create a Tessellation | |

treeprune | Prune Tree to Given Level | |

rex | Richardson Extrapolation | |

volume | Volume of an Object | |

unstack.ppp | Separate Multiple Columns of Marks | |

quad.object | Class of Quadrature Schemes | |

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

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

runiflpp | Uniform Random Points on a Linear Network | |

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

stienen | Stienen Diagram | |

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

psp.object | Class of Line Segment Patterns | |

square | Square Window | |

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

subspaceDistance | Distance Between Linear Spaces | |

shift.owin | Apply Vector Translation To Window | |

tilenames | Names of Tiles in a Tessellation | |

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

tile.lengths | Compute Lengths of Tiles in a Tessellation on a Network | |

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

varcount | Predicted Variance of the Number of Points | |

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

unstack.solist | Unstack Each Spatial Object in a List of Objects | |

text.ppp | Add Text Labels to Spatial Pattern | |

timeTaken | Extract the Total Computation Time | |

sumouter | Compute Quadratic Forms | |

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

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

vertices | Vertices of a Window | |

quasirandom | Quasirandom Patterns | |

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

trim.rectangle | Cut margins from rectangle | |

tiles | Extract List of Tiles in a Tessellation | |

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

superimpose | Superimpose Several Geometric Patterns | |

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

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

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

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

update.interact | Update an Interpoint Interaction | |

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

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

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

texturemap | Texture Map | |

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

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

stratrand | Stratified random point pattern | |

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

unitname | Name for Unit of Length | |

rThomas | Simulate Thomas Process | |

summary.distfun | Summarizing a Function of Spatial Location | |

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

timed | Record the Computation Time | |

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

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

rectdistmap | Distance Map Using Rectangular Distance Metric | |

zclustermodel | Cluster Point Process Model | |

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

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

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

varblock | Estimate Variance of Summary Statistic by Subdivision | |

rgbim | Create Colour-Valued Pixel Image | |

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

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

suffstat | Sufficient Statistic of Point Process Model | |

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

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

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

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

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

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

rotate.owin | Rotate a Window | |

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

zapsmall.im | Rounding of Pixel Values | |

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

rstrat | Simulate Stratified Random Point Pattern | |

scanLRTS | Likelihood Ratio Test Statistic for Scan Test | |

sdr | Sufficient Dimension Reduction | |

rotate.ppp | Rotate a Point Pattern | |

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

scan.test | Spatial Scan Test | |

scanpp | Read Point Pattern From Data File | |

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

slrm | Spatial Logistic Regression | |

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

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

summary.quad | Summarizing a Quadrature Scheme | |

symbolmap | Graphics Symbol Map | |

split.msr | Divide a Measure into Parts | |

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

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

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

whist | Weighted Histogram | |

unnormdensity | Weighted kernel smoother | |

textureplot | Plot Image or Tessellation Using Texture Fill | |

transect.im | Pixel Values Along a Transect | |

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

transmat | Convert Pixel Array Between Different Conventions | |

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

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

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

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

will.expand | Test Expansion Rule | |

Extract.fasp | Extract Subset of Function Array | |

CDF | Cumulative Distribution Function From Kernel Density Estimate | |

Emark | Diagnostics for random marking | |

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

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

DiggleGratton | Diggle-Gratton model | |

BadGey | Hybrid Geyer Point Process Model | |

AreaInter | The Area Interaction Point Process Model | |

Concom | The Connected Component Process Model | |

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 | 2018-07-27 |

License | GPL (>= 2) |

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

LazyData | true |

NeedsCompilation | yes |

ByteCompile | true |

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

Packaged | 2018-07-27 05:43:39 UTC; adrian |

Repository | CRAN |

Date/Publication | 2018-08-01 15:50:03 UTC |

imports | abind , deldir (>= 0.0-21) , goftest , Matrix , mgcv , polyclip (>= 1.5-0) , spatstat.utils (>= 1.9-0) , 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 (>= 1.3-0) , 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)
```