# spatstat v1.47-0

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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, Diggle-Cressie-Loosmore-Ford, Dao-Genton) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov) are also supported.
Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods.
A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.

## Functions in spatstat

Name | Description | |

affine.owin | Apply Affine Transformation To Window | |

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

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

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

addvar | Added Variable Plot for Point Process Model | |

affine | Apply Affine Transformation | |

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

add.texture | Fill Plot With Texture | |

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

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

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

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

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

anemones | Beadlet Anemones Data | |

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

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

angles.psp | Orientation Angles of Line Segments | |

amacrine | Hughes' Amacrine Cell Data | |

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

affine.tess | Apply Geometrical Transformation To Tessellation | |

ants | Harkness-Isham ants' nests data | |

anylist | List of Objects | |

areaGain | Difference of Disc Areas | |

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

area.owin | Area of a Window | |

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

append.psp | Combine Two Line Segment Patterns | |

AreaInter | The Area Interaction Point Process Model | |

areaLoss | Difference of Disc Areas | |

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

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

as.data.frame.hyperframe | Coerce Hyperframe 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.boxx | Convert Data to Multi-Dimensional Box | |

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

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

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

as.fv | Convert Data To Class fv | |

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

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

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

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

as.layered | Convert Data To Layered Object | |

as.hyperframe | Convert Data to Hyperframe | |

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

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

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

as.interact | Extract Interaction Structure | |

as.im | Convert to Pixel Image | |

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

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

as.owin | Convert Data To Class owin | |

as.ppm | Extract Fitted Point Process Model | |

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

as.psp | Convert Data To Class psp | |

as.ppp | Convert Data To Class ppp | |

as.rectangle | Window Frame | |

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

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

bc.ppm | Bias Correction for Fitted Model | |

BadGey | Hybrid Geyer Point Process Model | |

bdist.pixels | Distance to Boundary of Window | |

bdist.points | Distance to Boundary of Window | |

bdist.tiles | Distance to Boundary of Window | |

bdspots | Breakdown Spots in Microelectronic Materials | |

austates | Australian States and Mainland Territories | |

auc | Area Under ROC Curve | |

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

as.tess | Convert Data To Tessellation | |

blur | Apply Gaussian Blur to a Pixel Image | |

bind.fv | Combine Function Value Tables | |

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

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

CDF | Cumulative Distribution Function From Kernel Density Estimate | |

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

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

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

clickjoin | Interactively join vertices on a plot | |

border | Border Region of a Window | |

bronzefilter | Bronze gradient filter data | |

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

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

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

cells | Biological Cells Point Pattern | |

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

centroid.owin | Centroid of a window | |

clickpoly | Interactively Define a Polygon | |

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

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

clusterfield | Field of clusters | |

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

colourtools | Convert and Compare Colours in Different Formats | |

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

connected | Connected components | |

Concom | The Connected Component Process Model | |

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

begins | Check Start of Character String | |

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

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

bei | Tropical rain forest trees | |

box3 | Three-Dimensional Box | |

boxx | Multi-Dimensional Box | |

clmfires | Castilla-La Mancha Forest Fires | |

closepairs | Close Pairs of Points | |

clusterkernel | Extract Cluster Offspring Kernel | |

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

convexify | Weil's Convexifying Operation | |

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

boundingcircle | Smallest Enclosing Circle | |

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

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

clarkevans | Clark and Evans Aggregation Index | |

clarkevans.test | Clark and Evans Test | |

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

closing | Morphological Closing | |

compareFit | Residual Diagnostics for Multiple Fitted Models | |

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

crossdist | Pairwise distances | |

convolve.im | Convolution of Pixel Images | |

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

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

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

dfbetas.ppm | Parameter influence measure | |

diameter | Diameter of an Object | |

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

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

dirichlet | Dirichlet Tessellation of Point Pattern | |

clickdist | Interactively Measure Distance | |

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

beginner | Print Introduction For Beginners | |

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

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

chorley | Chorley-Ribble Cancer Data | |

circdensity | Density Estimation for Circular Data | |

clickbox | Interactively Define a Rectangle | |

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

complement.owin | Take Complement of a Window | |

colourmap | Colour Lookup Tables | |

concatxy | Concatenate x,y Coordinate Vectors | |

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

betacells | Beta Ganglion Cells in Cat Retina | |

bramblecanes | Hutchings' Bramble Canes data | |

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

branchlabelfun | Tree Branch Membership Labelling Function | |

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

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

chicago | Chicago Street Crime Data | |

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

clickppp | Interactively Add Points | |

compatible | Test Whether Objects Are Compatible | |

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

contour.imlist | Array of Contour Plots | |

contour.im | Contour plot of pixel image | |

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

covering | Cover Region with Discs | |

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

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

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

demopat | Artificial Data Point Pattern | |

deltametric | Delta Metric | |

demohyper | Demonstration Example of Hyperframe of Spatial Data | |

dilated.areas | Areas of Morphological Dilations | |

DiggleGratton | Diggle-Gratton model | |

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

discs | Union of Discs | |

dkernel | Kernel distributions and random generation | |

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

dmixpois | Mixed Poisson Distribution | |

clusterset | Allard-Fraley Estimator of Cluster Feature | |

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

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

convexhull | Convex Hull | |

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

convexhull.xy | Convex Hull of Points | |

copyExampleFiles | Copy Data Files for Example | |

corners | Corners of a rectangle | |

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

delaunay | Delaunay Triangulation of Point Pattern | |

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

dppCauchy | Generalized Cauchy Determinantal Point Process Model | |

dppeigen | Internal function calculating eig and index | |

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

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

edit.hyperframe | Invoke Text Editor on Hyperframe | |

dendrite | Dendritic Spines Data | |

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

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

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

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

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

distfun.lpp | Distance Map on Linear Network | |

distmap | Distance Map | |

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

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

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

copper | Berman-Huntington points and lines data | |

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

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

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

dppBessel | Bessel Type Determinantal Point Process Model | |

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

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

dppPowerExp | Power Exponential Spectral Determinantal Point Process Model | |

ellipse | Elliptical Window. | |

Emark | Diagnostics for random marking | |

eval.im | Evaluate Expression Involving Pixel Images | |

eval.fv | Evaluate Expression Involving Functions | |

eval.fasp | Evaluate Expression Involving Function Arrays | |

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

deriv.fv | Calculate Derivative of Function Values | |

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

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

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

dilation | Morphological Dilation | |

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

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

dppGauss | Gaussian Determinantal Point Process Model | |

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

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

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

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

endpoints.psp | Endpoints of Line Segment Pattern | |

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

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

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

finpines | Pine saplings in Finland. | |

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

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

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

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

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

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

dirichletVertices | Vertices and Edges of Dirichlet Tessellation | |

dirichletWeights | Compute Quadrature Weights Based on Dirichlet Tessellation | |

distmap.owin | Distance Map of Window | |

edge.Trans | Translation Edge Correction | |

distmap.ppp | Distance Map of Point Pattern | |

Gfox | Foxall's Distance Functions | |

Ginhom | Inhomogeneous Nearest Neighbour Function | |

Hardcore | The Hard Core Point Process Model | |

harmonic | Basis for Harmonic Functions | |

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

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

delaunayDistance | Distance on Delaunay Triangulation | |

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

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

diameter.owin | Diameter of a Window | |

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

discpartarea | Area of Part of Disc | |

disc | Circular Window | |

distcdf | Distribution Function of Interpoint Distance | |

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

Extract.fasp | Extract Subset of Function Array | |

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

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

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

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

edges | Extract Boundary Edges of a Window. | |

funxy | Spatial Function Class | |

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

emend | Force Model to be Valid | |

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

envelope | Simulation Envelopes of Summary Function | |

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

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

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

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

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

fryplot | Fry Plot of Point Pattern | |

Gres | Residual G Function | |

gridcentres | Rectangular grid of points | |

hextess | Hexagonal Grid or Tessellation | |

HierHard | The Hierarchical Hard Core Point Process Model | |

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

Iest | Estimate the I-function | |

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

intensity | Intensity of a Dataset or a Model | |

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

intersect.tess | Intersection of Two Tessellations | |

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

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

inforder.family | Infinite Order Interaction Family | |

insertVertices | Insert New Vertices in a Linear Network | |

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

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

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

japanesepines | Japanese Pines Point Pattern | |

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

distfun | Distance Map as a Function | |

dppm | Fit Determinantal Point Process Model | |

dppMatern | Whittle-Matern Determinantal Point Process Model | |

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

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

edges2triangles | List Triangles in a Graph | |

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

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

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

is.rectangle | Determine Type of Window | |

Kcom | Model Compensator of K Function | |

envelope.envelope | Recompute Envelopes | |

Kcross.inhom | Inhomogeneous Cross K Function | |

ewcdf | Weighted Empirical Cumulative Distribution Function | |

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

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

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

fasp.object | Function Arrays for Spatial Patterns | |

fv | Create a Function Value Table | |

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

Gcom | Model Compensator of Nearest Neighbour Function | |

gordon | People in Gordon Square | |

gorillas | Gorilla Nesting Sites | |

heather | Diggle's Heather Data | |

Hest | Spherical Contact Distribution Function | |

im.object | Class of Images | |

imcov | Spatial Covariance of a Pixel Image | |

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

interp.colourmap | Interpolate smoothly between specified colours | |

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

Jinhom | Inhomogeneous J-function | |

interp.im | Interpolate a Pixel Image | |

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

Jmulti | Marked J Function | |

Kmark | Mark-Weighted K Function | |

Kmeasure | Reduced Second Moment Measure | |

LennardJones | The Lennard-Jones Potential | |

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

Lest | L-function | |

domain | Extract the Domain of any Spatial Object | |

dirichletAreas | Compute Areas of Tiles in Dirichlet Tessellation | |

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

dppapproxkernel | Approximate Determinantal Point Process Kernel | |

eem | Exponential Energy Marks | |

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

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

erosion | Morphological Erosion by a Disc | |

edges2vees | List Dihedral Triples in a Graph | |

erosionAny | Morphological Erosion of Windows | |

Extract.owin | Extract Subset of Window | |

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

expand.owin | Apply Expansion Rule | |

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

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

Kdot.inhom | Inhomogeneous Multitype K Dot Function | |

Kinhom | Inhomogeneous K-function | |

LambertW | Lambert's W Function | |

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

lansing | Lansing Woods Point Pattern | |

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

linearKinhom | Inhomogeneous Linear K Function | |

lixellate | Subdivide Segments of a Network | |

Fiksel | The Fiksel Interaction | |

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

Finhom | Inhomogeneous Empty Space Function | |

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

fourierbasis | Fourier Basis Functions | |

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

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

gridweights | Compute Quadrature Weights Based on Grid Counts | |

localK | Neighbourhood density function | |

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

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

markvario | Mark Variogram | |

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

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

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

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

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

grow.rectangle | Add margins to rectangle | |

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

hierpair.family | Hierarchical Pairwise Interaction Process Family | |

hamster | Aherne's hamster tumour data | |

hyperframe | Hyper Data Frame | |

HierStrauss | The Hierarchical Strauss Point Process Model | |

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

Kest.fft | K-function using FFT | |

Kest | K-function | |

laslett | Laslett's Transform | |

Lcross.inhom | Inhomogeneous Cross Type L Function | |

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

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

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

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

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

linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |

localKinhom | Inhomogeneous Neighbourhood Density Function | |

localpcf | Local pair correlation function | |

logLik.slrm | Loglikelihood of Spatial Logistic Regression | |

markcorr | Mark Correlation Function | |

lohboot | Bootstrap Confidence Bands for Summary Function | |

markcrosscorr | Mark Cross-Correlation Function | |

eroded.areas | Areas of Morphological Erosions | |

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

Extract.im | Extract Subset of Image | |

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

Extract.linnet | Extract Subset of Linear Network | |

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

Extract.quad | Subset of Quadrature Scheme | |

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

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

flu | Influenza Virus Proteins | |

hyytiala | Scots pines and other trees at Hyytiala | |

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

incircle | Find Largest Circle Inside Window | |

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

intensity.quadratcount | Intensity Estimates Using Quadrat Counts | |

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

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

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

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

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

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

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

moribund | Outdated Functions | |

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

multiplicity.ppp | Count Multiplicity of Duplicate Points | |

nztrees | New Zealand Trees Point Pattern | |

MultiHard | The Multitype Hard Core Point Process Model | |

objsurf | Objective Function Surface | |

pairdist | Pairwise distances | |

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

kernel.factor | Scale factor for density kernel | |

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

Jest | Estimate the J-function | |

Kres | Residual K Function | |

kppm | Fit Cluster or Cox Point Process Model | |

Ldot.inhom | Inhomogeneous Multitype L Dot Function | |

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

letterR | Window in Shape of Letter R | |

levelset | Level Set of a Pixel Image | |

Linhom | L-function | |

mean.im | Maximum, Minimum, Mean, Median, Range or Sum of Pixel Values in an Image | |

methods.fii | Methods for Fitted Interactions | |

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

methods.funxy | Methods for Spatial Functions | |

miplot | Morisita Index Plot | |

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

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

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

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

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

paracou | Kimboto trees at Paracou, French Guiana | |

pcfmulti | Marked pair correlation function | |

Penttinen | Penttinen Interaction | |

plot.cdftest | Plot a Spatial Distribution Test | |

plot.colourmap | Plot a Colour Map | |

plot.fasp | Plot a Function Array | |

plot.fv | Plot Function Values | |

plot.layered | Layered Plot | |

plot.leverage.ppm | Plot Leverage Function | |

linearpcf | Linear Pair Correlation Function | |

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

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

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

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

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

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

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

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

FmultiInhom | Inhomogeneous Marked F-Function | |

model.images | Compute Images of Constructed Covariates | |

Gest | Nearest Neighbour Distance Function G | |

harmonise.fv | Make Function Tables Compatible | |

Geyer | Geyer's Saturation Point Process Model | |

harmonise.im | Make Pixel Images Compatible | |

hybrid.family | Hybrid Interaction Family | |

integral.im | Integral of a Pixel Image | |

Hybrid | Hybrid Interaction Point Process Model | |

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

integral.msr | Integral of a Measure | |

integral.linim | Integral on a Linear Network | |

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

plot.textstring | Plot a Text String | |

plot.texturemap | Plot a Texture Map | |

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

pool.fv | Pool Several Functions | |

ppp | Create a Point Pattern | |

ppp.object | Class of Point Patterns | |

ppx | Multidimensional Space-Time Point Pattern | |

linim | Create Pixel Image on Linear Network | |

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

marks.tess | Marks of a Tessellation | |

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

lurking | Lurking variable plot | |

methods.linnet | Methods for Linear Networks | |

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

midpoints.psp | Midpoints of Line Segment Pattern | |

methods.zclustermodel | Methods for Cluster Models | |

project2segment | Move Point To Nearest Line | |

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

MultiStrauss | The Multitype Strauss Point Process Model | |

project2set | Find Nearest Point in a Region | |

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

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

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

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

quasirandom | Quasirandom Patterns | |

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

reflect | Reflect In Origin | |

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

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

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

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

is.marked | Test Whether Marks Are Present | |

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

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

kernel.moment | Moment of Smoothing Kernel | |

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

fardist | Farthest Distance to Boundary of Window | |

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

kernel.squint | Integral of Squared Kernel | |

flipxy | Exchange X and Y Coordinates | |

foo | Foo is Not a Real Name | |

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

fv.object | Function Value Table | |

Gmulti | Marked Nearest Neighbour Distance Function | |

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

rGaussPoisson | Simulate Gauss-Poisson Process | |

harmonise | Make Objects Compatible | |

GmultiInhom | Inhomogeneous Marked G-Function | |

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

rgbim | Create Colour-Valued Pixel Image | |

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

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

roc | Receiver Operating Characteristic | |

rose | Rose Diagram | |

rpoislinetess | Poisson Line Tessellation | |

rpoisline | Generate Poisson Random Line Process | |

msr | Signed or Vector-Valued Measure | |

mucosa | Cells in Gastric Mucosa | |

rppm | Recursively Partitioned Point Process Model | |

nestsplit | Nested Split | |

nnclean | Nearest Neighbour Clutter Removal | |

nnfun | Nearest Neighbour Index Map as a Function | |

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

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

pairdist.ppx | Pairwise Distances in Any Dimensions | |

nvertices | Count Number of Vertices | |

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

pairdist.psp | Pairwise distances between line segments | |

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

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

nnorient | Nearest Neighbour Orientation Distribution | |

nnwhich | Nearest neighbour | |

opening | Morphological Opening | |

ord.family | Ord Interaction Process Family | |

owin | Create a Window | |

owin.object | Class owin | |

pairs.im | Scatterplot Matrix for Pixel Images | |

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

nnwhich.ppx | Nearest Neighbours in Any Dimensions | |

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

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

overlap.owin | Compute Area of Overlap | |

pairorient | Point Pair Orientation Distribution | |

parameters | Extract Model Parameters in Understandable Form | |

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

parres | Partial Residuals for Point Process Model | |

harmonise.owin | Make Windows Compatible | |

humberside | Humberside Data on Childhood Leukaemia and Lymphoma | |

hopskel | Hopkins-Skellam Test | |

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

im | Create a Pixel Image Object | |

infline | Infinite Straight Lines | |

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

intensity.ppp | Empirical Intensity of Point Pattern | |

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

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

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

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

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

pcfinhom | Inhomogeneous Pair Correlation Function | |

plot.laslett | Plot Laslett Transform | |

plot.owin | Plot a Spatial Window | |

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

pppmatching.object | Class of Point Matchings | |

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

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

Kscaled | Locally Scaled K-function | |

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

Ksector | Sector K-function | |

layered | Create List of Plotting Layers | |

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

linearK | Linear K Function | |

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

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

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

linequad | Quadrature Scheme on a Linear Network | |

linfun | Function on a Linear Network | |

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

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

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

marks | Marks of a Point Pattern | |

matchingdist | Distance for a Point Pattern Matching | |

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

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

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

methods.objsurf | Methods for Objective Function Surfaces | |

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

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

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

nearestsegment | Find Line Segment Nearest to Each Point | |

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

nndist | Nearest neighbour distances | |

npfun | Dummy Function Returns Number of Points | |

nnwhich.pp3 | Nearest neighbours in three dimensions | |

npoints | Number of Points in a Point Pattern | |

pixellate.owin | Convert Window to Pixel Image | |

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

plot.imlist | Plot a List of Images | |

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

plot.influence.ppm | Plot Influence Measure | |

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

plot.onearrow | Plot an Arrow | |

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

plot.symbolmap | Plot a Graphics Symbol Map | |

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

plot.tess | Plot a tessellation | |

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

persp.im | Perspective Plot of Pixel Image | |

pixelcentres | Extract Pixel Centres as Point Pattern | |

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

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

pixellate | Convert Spatial Object to Pixel Image | |

plot.envelope | Plot a Simulation Envelope | |

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

plot.listof | Plot a List of Things | |

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

is.multitype | Test whether Object is Multitype | |

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

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

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

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

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

Kmulti.inhom | Inhomogeneous Marked K-Function | |

Kovesi | Colour Sequences with Uniform Perceptual Contrast | |

Ord | Generic Ord Interaction model | |

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

OrdThresh | Ord's Interaction model | |

pairdist.pp3 | Pairwise distances in Three Dimensions | |

pairdist.ppp | Pairwise distances | |

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

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

perimeter | Perimeter Length of Window | |

periodify | Make Periodic Copies of a Spatial Pattern | |

plot.bermantest | Plot Result of Berman Test | |

plot.anylist | Plot a List of Things | |

lengths.psp | Lengths of Line Segments | |

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

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

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

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

linnet | Create a Linear Network | |

lintess | Tessellation on a Linear Network | |

longleaf | Longleaf Pines Point Pattern | |

lpp | Create Point Pattern on Linear Network | |

lut | Lookup Tables | |

markconnect | Mark Connection Function | |

mergeLevels | Merge Levels of a Factor | |

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

methods.layered | Methods for Layered Objects | |

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

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

mincontrast | Method of Minimum Contrast | |

methods.units | Methods for Units | |

MinkowskiSum | Minkowski Sum of Windows | |

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

murchison | Murchison gold deposits | |

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

quad.object | Class of Quadrature Schemes | |

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

relrisk | Estimate of Spatially-Varying Relative Risk | |

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

requireversion | Require a Specific Version of a Package | |

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

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

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

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

plot.ppp | plot a Spatial Point Pattern | |

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

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

pointsOnLines | Place Points Evenly Along Specified Lines | |

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

pool.envelope | Pool Data from Several Envelopes | |

ppm | Fit Point Process Model to Data | |

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

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

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

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

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

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

rcell | Simulate Baddeley-Silverman Cell Process | |

quantile.im | Sample Quantiles of Pixel Image | |

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

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

plot.quad | Plot a Spatial Quadrature Scheme | |

plot.quadratcount | Plot Quadrat Counts | |

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

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

Poisson | Poisson Point Process Model | |

ponderosa | Ponderosa Pine Tree Point Pattern | |

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

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

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

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

rescue.rectangle | Convert Window Back To Rectangle | |

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

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

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

rMatClust | Simulate Matern Cluster Process | |

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

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

rmpoint | Generate N Random Multitype Points | |

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

rmpoispp | Generate Multitype Poisson Point Pattern | |

rLGCP | Simulate Log-Gaussian Cox Process | |

rlabel | Random Re-Labelling of Point Pattern | |

rmhexpand | Specify Simulation Window or Expansion Rule | |

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

rMosaicSet | Mosaic Random Set | |

rMosaicField | Mosaic Random Field | |

rotate.owin | Rotate a Window | |

rotate.ppp | Rotate a Point Pattern | |

rshift | Random Shift | |

rshift.ppp | Randomly Shift a Point Pattern | |

pp3 | Three Dimensional Point Pattern | |

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

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

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

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

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

print.quad | Print a Quadrature Scheme | |

pyramidal | Pyramidal Neurons in Cingulate Cortex | |

quantile.density | Quantiles of a Density Estimate | |

quantess | Quantile Tessellation | |

nbfires | Point Patterns of New Brunswick Forest Fires | |

nncross | Nearest Neighbours Between Two Patterns | |

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

padimage | Pad the Border of a Pixel Image | |

nnmap | K-th Nearest Point Map | |

nnmark | Mark of Nearest Neighbour | |

pairdist.default | Pairwise distances | |

pairwise.family | Pairwise Interaction Process Family | |

Pairwise | Generic Pairwise Interaction model | |

pcf | Pair Correlation Function | |

rsyst | Simulate systematic random point pattern | |

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

runiflpp | Uniform Random Points on a Linear Network | |

scalardilate | Apply Scalar Dilation | |

runifpoint | Generate N Uniform Random Points | |

scaletointerval | Rescale Data to Lie Between Specified Limits | |

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

simdat | Simulated Point Pattern | |

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

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

psp | Create a Line Segment Pattern | |

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

psp.object | Class of Line Segment Patterns | |

quadratcount | Quadrat counting for a point pattern | |

range.fv | Range of Function Values | |

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

rdpp | Simulation of a Determinantal Point Process | |

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

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

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

rounding | Detect Numerical Rounding | |

rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |

rPoissonCluster | Simulate Poisson Cluster Process | |

rSSI | Simulate Simple Sequential Inhibition | |

rstrat | Simulate Stratified Random Point Pattern | |

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

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

scan.test | Spatial Scan Test | |

scanLRTS | Likelihood Ratio Test Statistic for Scan Test | |

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

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

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

reach | Interaction Distance of a Point Process | |

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

rescale | Convert dataset to another unit of length | |

rHardcore | Perfect Simulation of the Hardcore Process | |

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

rMaternI | Simulate Matern Model I | |

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

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

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

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

pixelquad | Quadrature Scheme Based on Pixel Grid | |

plot.hyperframe | Plot Entries in a Hyperframe | |

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

plot.im | Plot a Pixel Image | |

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

plot.linnet | Plot a linear network | |

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

rMaternII | Simulate Matern Model II | |

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

rNeymanScott | Simulate Neyman-Scott Process | |

rnoise | Random Pixel Noise | |

rPenttinen | Perfect Simulation of the Penttinen Process | |

rpoint | Generate N Random Points | |

rthin | Random Thinning | |

rThomas | Simulate Thomas Process | |

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

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

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

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

shapley | Galaxies in the Shapley Supercluster | |

sharpen | Data Sharpening of Point Pattern | |

simplenet | Simple Example of Linear Network | |

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

Softcore | The Soft Core Point Process Model | |

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

Strauss | The Strauss Point Process Model | |

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

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

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

suffstat | Sufficient Statistic of Point Process Model | |

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

superimpose | Superimpose Several Geometric Patterns | |

sumouter | Compute Quadratic Forms | |

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

unmark | Remove Marks | |

unnormdensity | Weighted kernel smoother | |

tiles | Extract List of Tiles in a Tessellation | |

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

urkiola | Urkiola Woods Point Pattern | |

varblock | Estimate Variance of Summary Statistic by Subdivision | |

varcount | Predicted Variance of the Number of Points | |

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

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

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

zclustermodel | Cluster Point Process Model | |

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

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

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

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

spokes | Spokes pattern of dummy points | |

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

summary.owin | Summary of a Spatial Window | |

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

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

trim.rectangle | Cut margins from rectangle | |

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

summary.quad | Summarizing a Quadrature Scheme | |

Tstat | Third order summary statistic | |

Triplets | The Triplet Point Process Model | |

triplet.family | Triplet Interaction Family | |

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

update.interact | Update an Interpoint Interaction | |

spatdim | Spatial Dimension of a Dataset | |

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

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

square | Square Window | |

spatstat-deprecated | Deprecated spatstat functions | |

spatialcdf | Spatial Cumulative Distribution Function | |

summary.im | Summarizing a Pixel Image | |

stieltjes | Compute Integral of Function Against Cumulative Distribution | |

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

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

swedishpines | Swedish Pines Point Pattern | |

rQuasi | Generate Quasirandom Point Pattern in Given Window | |

rStrauss | Perfect Simulation of the Strauss Process | |

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

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

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

shift.owin | Apply Vector Translation To Window | |

scanpp | Read Point Pattern From Data File | |

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

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

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

WindowOnly | Extract Window of Spatial Object | |

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

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

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

zapsmall.im | Rounding of Pixel Values | |

rex | Richardson Extrapolation | |

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

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

ripras | Estimate window from points alone | |

rotate.im | Rotate a Pixel Image | |

rotate | Rotate | |

spatstat.options | Internal Options in Spatstat Package | |

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

solist | List of Two-Dimensional Spatial Objects | |

rpoislpp | Poisson Point Process on a Linear Network | |

rpoispp | Generate Poisson Point Pattern | |

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

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

spruces | Spruces Point Pattern | |

sporophores | Sporophores Data | |

studpermu.test | Studentised Permutation Test | |

subfits | Extract List of Individual Point Process Models | |

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

tileindex | Determine Which Tile Contains Each Given Point | |

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

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

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

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

shift | Apply Vector Translation | |

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

slrm | Spatial Logistic Regression | |

Smooth.fv | Apply Smoothing to Function Values | |

Smooth | Spatial smoothing of data | |

split.msr | Divide a Measure into Parts | |

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

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

pool.quadrattest | Pool Several Quadrat Tests | |

pool | Pool Data | |

pppmatching | Create a Point Matching | |

pppdist | Distance Between Two Point Patterns | |

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

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

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

progressreport | Print Progress Reports | |

tilenames | Names of Tiles in a Tessellation | |

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

unitname | Name for Unit of Length | |

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

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

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

volume | Volume of an Object | |

waka | Trees in Waka national park | |

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

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

quadratresample | Resample a Point Pattern by Resampling Quadrats | |

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

quadrats | Divide Region into Quadrats | |

rat | Ratio object | |

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

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

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

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

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

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

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

textureplot | Plot Image or Tessellation Using Texture Fill | |

texturemap | Texture Map | |

transmat | Convert Pixel Array Between Different Conventions | |

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

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

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

vertices | Vertices of a Window | |

vesicles | Vesicles Data | |

rknn | Theoretical Distribution of Nearest Neighbour Distance | |

rjitter | Random Perturbation of a Point Pattern | |

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

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

rotate.psp | Rotate a Line Segment Pattern | |

rotmean | Rotational Average of a Pixel Image | |

rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |

rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |

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

Saturated | Saturated Pairwise Interaction model | |

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

setcov | Set Covariance of a Window | |

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

spatstat-internal | Internal spatstat functions | |

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

spatstat-package | The Spatstat Package | |

stienen | Stienen Diagram | |

stratrand | Stratified random point pattern | |

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

symbolmap | Graphics Symbol Map | |

tess | Create a Tessellation | |

treeprune | Prune Tree to Given Level | |

triangulate.owin | Decompose Window into Triangles | |

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

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

valid | Check Whether Point Process Model is Valid | |

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

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

whist | Weighted Histogram | |

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

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

timed | Record the Computation Time | |

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

transect.im | Pixel Values Along a Transect | |

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

unstack.ppp | Separate Multiple Columns of Marks | |

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

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

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

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

will.expand | Test Expansion Rule | |

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

No Results! |

## Last month downloads

## Details

Nickname | Responsible Gambler |

Date | 2016-10-12 |

License | GPL (>= 2) |

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

LazyData | true |

NeedsCompilation | yes |

ByteCompile | true |

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

Packaged | 2016-10-12 08:04:08 UTC; adrian |

Repository | CRAN |

Date/Publication | 2016-10-13 01:00:59 |

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

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

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

Contributors | Adrian Baddeley, Rolf Turner, Ege Rubak |

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