# spatstat v1.63-2

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## Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests

Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images.
Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference.
Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks.
Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods.
A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.

## Functions in spatstat

Name | Description | |

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

Ginhom | Inhomogeneous Nearest Neighbour Function | |

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

HierHard | The Hierarchical Hard Core Point Process Model | |

Kscaled | Locally Scaled K-function | |

Emark | Diagnostics for random marking | |

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

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

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

Kest | K-function | |

Concom | The Connected Component Process Model | |

Jest | Estimate the J-function | |

Fiksel | The Fiksel Interaction | |

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

Iest | Estimate the I-function | |

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

Extract.fasp | Extract Subset of Function Array | |

HierStrauss | The Hierarchical Strauss Point Process Model | |

AreaInter | The Area Interaction Point Process Model | |

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

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

Smooth.fv | Apply Smoothing to Function Values | |

Hardcore | The Hard Core Point Process Model | |

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

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

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

Kest.fft | K-function using FFT | |

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

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

BadGey | Hybrid Geyer Point Process Model | |

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

Smooth | Spatial smoothing of data | |

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

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

Kdot.inhom | Inhomogeneous Multitype K Dot Function | |

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

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

Gest | Nearest Neighbour Distance Function G | |

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

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

Extract.im | Extract Subset of Image | |

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

GmultiInhom | Inhomogeneous Marked G-Function | |

[.ssf | Subset of spatially sampled function | |

CDF | Cumulative Distribution Function From Kernel Density Estimate | |

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

FmultiInhom | Inhomogeneous Marked F-Function | |

Ksector | Sector K-function | |

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

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

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

Finhom | Inhomogeneous Empty Space Function | |

Lest | L-function | |

area.owin | Area of a Window | |

Kmark | Mark-Weighted K Function | |

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

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

Lcross.inhom | Inhomogeneous Cross Type L Function | |

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

Hybrid | Hybrid Interaction Point Process Model | |

MultiHard | The Multitype Hard Core Point Process Model | |

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

Gmulti | Marked Nearest Neighbour Distance Function | |

Ops.msr | Arithmetic Operations on Measures | |

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

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

Hest | Spherical Contact Distribution Function | |

Extract.quad | Subset of Quadrature Scheme | |

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

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

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

Ord | Generic Ord Interaction model | |

Jmulti | Marked J Function | |

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

LambertW | Lambert's W Function | |

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

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

add.texture | Fill Plot With Texture | |

affine.owin | Apply Affine Transformation To Window | |

MinkowskiSum | Minkowski Sum of Windows | |

Kinhom | Inhomogeneous K-function | |

Kmulti | Marked K-Function | |

Jinhom | Inhomogeneous J-function | |

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

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

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

Gres | Residual G Function | |

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

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

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

beginner | Print Introduction For Beginners | |

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

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

affine.tess | Apply Geometrical Transformation To Tessellation | |

anylist | List of Objects | |

OrdThresh | Ord's Interaction model | |

Kmulti.inhom | Inhomogeneous Marked K-Function | |

areaLoss | Difference of Disc Areas | |

Linhom | Inhomogeneous L-function | |

Kcross.inhom | Inhomogeneous Cross K Function | |

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

Ldot.inhom | Inhomogeneous Multitype L Dot Function | |

areaGain | Difference of Disc Areas | |

begins | Check Start of Character String | |

Kres | Residual K Function | |

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

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

Saturated | Saturated Pairwise Interaction model | |

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

Extract.linnet | Extract Subset of Linear Network | |

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

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

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

append.psp | Combine Two Line Segment Patterns | |

MultiStrauss | The Multitype Strauss Point Process Model | |

as.psp | Convert Data To Class psp | |

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

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

Triplets | The Triplet Point Process Model | |

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

Smooth.ssf | Smooth a Spatially Sampled Function | |

as.ppm | Extract Fitted Point Process Model | |

bind.fv | Combine Function Value Tables | |

as.interact | Extract Interaction Structure | |

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

Tstat | Third order summary statistic | |

closing | Morphological Closing | |

addvar | Added Variable Plot for Point Process Model | |

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

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

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

as.fv | Convert Data To Class fv | |

Gcom | Model Compensator of Nearest Neighbour Function | |

as.ppp | Convert Data To Class ppp | |

DiggleGratton | Diggle-Gratton model | |

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

complement.owin | Take Complement of a Window | |

as.layered | Convert Data To Layered Object | |

Extract.owin | Extract Subset of Window | |

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

concatxy | Concatenate x,y Coordinate Vectors | |

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

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

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

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

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

WindowOnly | Extract Window of Spatial Object | |

Softcore | The Soft Core Point Process Model | |

branchlabelfun | Tree Branch Membership Labelling Function | |

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

as.rectangle | Window Frame | |

as.tess | Convert Data To Tessellation | |

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

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

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

colourmap | Colour Lookup Tables | |

angles.psp | Orientation Angles of Line Segments | |

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

border | Border Region of a Window | |

affine | Apply Affine Transformation | |

circdensity | Density Estimation for Circular Data | |

ellipse | Elliptical Window. | |

auc | Area Under ROC Curve | |

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

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

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

bc.ppm | Bias Correction for Fitted Model | |

Geyer | Geyer's Saturation Point Process Model | |

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

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

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

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

Pairwise | Generic Pairwise Interaction model | |

as.hyperframe | Convert Data to Hyperframe | |

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

closepairs | Close Pairs of Points | |

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

Kcom | Model Compensator of K Function | |

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

clickdist | Interactively Measure Distance | |

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

Kmeasure | Reduced Second Moment Measure | |

Gfox | Foxall's Distance Functions | |

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

delaunayDistance | Distance on Delaunay Triangulation | |

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

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

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

clickjoin | Interactively join vertices on a plot | |

bugfixes | List Recent Bug Fixes | |

connected | Connected components | |

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

convexify | Weil's Convexifying Operation | |

clarkevans | Clark and Evans Aggregation Index | |

clarkevans.test | Clark and Evans Test | |

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

clickppp | Interactively Add Points | |

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

bdist.pixels | Distance to Boundary of Window | |

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

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

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

eval.fv | Evaluate Expression Involving Functions | |

convolve.im | Convolution of Pixel Images | |

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

blur | Apply Gaussian Blur to a Pixel Image | |

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

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

Penttinen | Penttinen Interaction | |

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

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

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

LennardJones | The Lennard-Jones Potential | |

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

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

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

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

clicklpp | Interactively Add Points on a Linear Network | |

clickbox | Interactively Define a Rectangle | |

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

Poisson | Poisson Point Process Model | |

bdist.points | Distance to Boundary of Window | |

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

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

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

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

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

Strauss | The Strauss Point Process Model | |

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

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

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

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

convexhull.xy | Convex Hull of Points | |

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

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

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

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

clusterkernel | Extract Cluster Offspring Kernel | |

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

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

box3 | Three-Dimensional Box | |

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

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

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

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

boxx | Multi-Dimensional Box | |

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

bw.abram | Abramson's Adaptive Bandwidths | |

clickpoly | Interactively Define a Polygon | |

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

dimhat | Estimate Dimension of Central Subspace | |

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

dmixpois | Mixed Poisson Distribution | |

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

compareFit | Residual Diagnostics for Multiple Fitted Models | |

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

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

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

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

compatible | Test Whether Objects Are Compatible | |

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

discpartarea | Area of Part of Disc | |

dilation | Morphological Dilation | |

contour.im | Contour plot of pixel image | |

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

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

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

clusterset | Allard-Fraley Estimator of Cluster Feature | |

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

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

contour.imlist | Array of Contour Plots | |

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

delaunay | Delaunay Triangulation of Point Pattern | |

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

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

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

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

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

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

dfbetas.ppm | Parameter Influence Measure | |

domain | Extract the Domain of any Spatial Object | |

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

edges | Extract Boundary Edges of a Window. | |

closetriples | Close Triples of Points | |

envelope.envelope | Recompute Envelopes | |

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

covering | Cover Region with Discs | |

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

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

dirichletWeights | Compute Quadrature Weights Based on Dirichlet Tessellation | |

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

dppBessel | Bessel Type Determinantal Point Process Model | |

convexhull | Convex Hull | |

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

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

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

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

eval.im | Evaluate Expression Involving Pixel Images | |

disc | Circular Window | |

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

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

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

distmap.ppp | Distance Map of Point Pattern | |

dirichlet | Dirichlet Tessellation of Point Pattern | |

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

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

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

dppCauchy | Generalized Cauchy Determinantal Point Process Model | |

as.im | Convert to Pixel Image | |

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

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

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

edges2triangles | List Triangles in a Graph | |

bdist.tiles | Distance to Boundary of Window | |

incircle | Find Largest Circle Inside Window | |

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

as.owin | Convert Data To Class owin | |

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

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

crossdist | Pairwise distances | |

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

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

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

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

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

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

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

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

fv.object | Function Value Table | |

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

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

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

envelopeArray | Array of Simulation Envelopes of Summary Function | |

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

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

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

boundingcircle | Smallest Enclosing Circle | |

harmonise.msr | Make Measures Compatible | |

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

harmonise.owin | Make Windows Compatible | |

intensity.ppp | Empirical Intensity of Point Pattern | |

dirichletVertices | Vertices and Edges of Dirichlet Tessellation | |

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

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

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

deriv.fv | Calculate Derivative of Function Values | |

dppm | Fit Determinantal Point Process Model | |

infline | Infinite Straight Lines | |

dirichletAreas | Compute Areas of Tiles in Dirichlet Tessellation | |

ewcdf | Weighted Empirical Cumulative Distribution Function | |

fv | Create a Function Value Table | |

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

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

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

increment.fv | Increments of a Function | |

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

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

erosionAny | Morphological Erosion of Windows | |

integral.im | Integral of a Pixel Image | |

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

discs | Union of Discs | |

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

im.object | Class of Images | |

grow.rectangle | Add margins to rectangle | |

distmap.owin | Distance Map of Window | |

distmap | Distance Map | |

dppPowerExp | Power Exponential Spectral Determinantal Point Process Model | |

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

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

dppapproxkernel | Approximate Determinantal Point Process Kernel | |

clusterfield | Field of clusters | |

edit.hyperframe | Invoke Text Editor on Hyperframe | |

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

edges2vees | List Dihedral Triples in a Graph | |

distcdf | Distribution Function of Interpoint Distance | |

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

lengths.psp | Lengths of Line Segments | |

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

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

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

harmonise.fv | Make Function Tables Compatible | |

centroid.owin | Centroid of a window | |

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

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

insertVertices | Insert New Vertices in a Linear Network | |

intensity.quadratcount | Intensity Estimates Using Quadrat Counts | |

kernel.factor | Scale factor for density kernel | |

eroded.areas | Areas of Morphological Erosions | |

gridcentres | Rectangular grid of points | |

eval.fasp | Evaluate Expression Involving Function Arrays | |

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

eem | Exponential Energy Marks | |

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

erosion | Morphological Erosion by a Disc | |

dppeigen | Internal function calculating eig and index | |

dppMatern | Whittle-Matern Determinantal Point Process Model | |

hybrid.family | Hybrid Interaction Family | |

is.multitype | Test whether Object is Multitype | |

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

foo | Foo is Not a Real Name | |

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

fardist | Farthest Distance to Boundary of Window | |

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

im | Create a Pixel Image Object | |

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

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

flipxy | Exchange X and Y Coordinates | |

dkernel | Kernel distributions and random generation | |

colourtools | Convert and Compare Colours in Different Formats | |

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

emend | Force Model to be Valid | |

hopskel | Hopkins-Skellam Test | |

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

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

linearKinhom | Inhomogeneous Linear K Function | |

integral.linim | Integral on a Linear Network | |

gridweights | Compute Quadrature Weights Based on Grid Counts | |

distfun | Distance Map as a Function | |

fryplot | Fry Plot of Point Pattern | |

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

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

funxy | Spatial Function Class | |

intensity | Intensity of a Dataset or a Model | |

kernel.moment | Moment of Smoothing Kernel | |

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

harmonic | Basis for Harmonic Functions | |

hextess | Hexagonal Grid or Tessellation | |

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

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

harmonise | Make Objects Compatible | |

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

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

edge.Trans | Translation Edge Correction | |

levelset | Level Set of a Pixel Image | |

kernel.squint | Integral of Squared Kernel | |

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

hierpair.family | Hierarchical Pairwise Interaction Process Family | |

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

linim | Create Pixel Image on Linear Network | |

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

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

harmonise.im | Make Pixel Images Compatible | |

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

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

joinVertices | Join Vertices in a Network | |

lineardirichlet | Dirichlet Tessellation on a Linear Network | |

lixellate | Subdivide Segments of a Network | |

lintess | Tessellation on a Linear Network | |

nndist | Nearest neighbour distances | |

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

interp.colourmap | Interpolate smoothly between specified colours | |

integral.msr | Integral of a Measure | |

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

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

corners | Corners of a rectangle | |

methods.fii | Methods for Fitted Interactions | |

imcov | Spatial Covariance of a Pixel Image | |

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

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

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

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

interp.im | Interpolate a Pixel Image | |

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

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

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

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

lurking | Lurking Variable Plot | |

is.marked | Test Whether Marks Are Present | |

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

deltametric | Delta Metric | |

localK | Neighbourhood density function | |

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

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

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

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

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

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

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

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

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

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

linnet | Create a Linear Network | |

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

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

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

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

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

methods.ssf | Methods for Spatially Sampled Functions | |

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

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

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

diameter | Diameter of an Object | |

diameter.owin | Diameter of a Window | |

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

dilated.areas | Areas of Morphological Dilations | |

distfun.lpp | Distance Map on Linear Network | |

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

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

nnmap | K-th Nearest Point Map | |

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

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

laslett | Laslett's Transform | |

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

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

linearK | Linear K Function | |

kppm | Fit Cluster or Cox Point Process Model | |

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

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

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

markcorr | Mark Correlation Function | |

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

localKcross.inhom | Inhomogeneous Multitype K Function | |

linearpcf | Linear Pair Correlation Function | |

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

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

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

linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |

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

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

markcrosscorr | Mark Cross-Correlation Function | |

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

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

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

markvario | Mark Variogram | |

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

dppGauss | Gaussian Determinantal Point Process Model | |

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

nnmark | Mark of Nearest Neighbour | |

pcf | Pair Correlation Function | |

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

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

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

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

lpp | Create Point Pattern on Linear Network | |

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

npfun | Dummy Function Returns Number of Points | |

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

npoints | Number of Points in a Point Pattern | |

lohboot | Bootstrap Confidence Bands for Summary Function | |

endpoints.psp | Endpoints of Line Segment Pattern | |

envelope | Simulation Envelopes of Summary Function | |

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

nestsplit | Nested Split | |

fasp.object | Function Arrays for Spatial Patterns | |

methods.layered | Methods for Layered Objects | |

methods.objsurf | Methods for Objective Function Surfaces | |

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

lut | Lookup Tables | |

matchingdist | Distance for a Point Pattern Matching | |

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

owin | Create a Window | |

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

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

localpcf | Local pair correlation function | |

pairdist.pp3 | Pairwise distances in Three Dimensions | |

owin.object | Class owin | |

marks.tess | Marks of a Tessellation | |

logLik.slrm | Loglikelihood of Spatial Logistic Regression | |

measureContinuous | Discrete and Continuous Components of a Measure | |

plot.layered | Layered Plot | |

pcfinhom | Inhomogeneous Pair Correlation Function | |

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

plot.laslett | Plot Laslett Transform | |

plot.bermantest | Plot Result of Berman Test | |

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

markconnect | Mark Connection Function | |

nearestsegment | Find Line Segment Nearest to Each Point | |

expand.owin | Apply Expansion Rule | |

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

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

methods.unitname | Methods for Units | |

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

plot.cdftest | Plot a Spatial Distribution Test | |

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

nnwhich.ppx | Nearest Neighbours in Any Dimensions | |

msr | Signed or Vector-Valued Measure | |

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

padimage | Pad the Border of a Pixel Image | |

mergeLevels | Merge Levels of a Factor | |

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

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

quadratcount | Quadrat counting for a point pattern | |

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

plot.onearrow | Plot an Arrow | |

methods.distfun | Geometrical Operations for Distance Functions | |

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

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

miplot | Morisita Index Plot | |

rHardcore | Perfect Simulation of the Hardcore Process | |

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

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

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

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

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

fourierbasis | Fourier Basis Functions | |

objsurf | Objective Function Surface | |

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

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

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

pixelcentres | Extract Pixel Centres as Point Pattern | |

midpoints.psp | Midpoints of Line Segment Pattern | |

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

methods.zclustermodel | Methods for Cluster Models | |

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

mincontrast | Method of Minimum Contrast | |

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

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

nearestValue | Image of Nearest Defined Pixel Value | |

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

hyperframe | Hyper Data Frame | |

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

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

pairdist.ppx | Pairwise Distances in Any Dimensions | |

intersect.tess | Intersection of Two Tessellations | |

nncross | Nearest Neighbours Between Two Patterns | |

nnclean | Nearest Neighbour Clutter Removal | |

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

plot.ssf | Plot a Spatially Sampled Function | |

overlap.owin | Compute Area of Overlap | |

periodify | Make Periodic Copies of a Spatial Pattern | |

ppp.object | Class of Point Patterns | |

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

ord.family | Ord Interaction Process Family | |

plot.studpermutest | Plot a Studentised Permutation Test | |

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

pool.envelope | Pool Data from Several Envelopes | |

plot.textstring | Plot a Text String | |

pcfmulti | Marked pair correlation function | |

pppdist | Distance Between Two Point Patterns | |

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

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

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

nnwhich.pp3 | Nearest neighbours in three dimensions | |

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

pixellate | Convert Spatial Object to Pixel Image | |

nnfun | Nearest Neighbour Index Map as a Function | |

pairdist | Pairwise distances | |

plot.leverage.ppm | Plot Leverage Function | |

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

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

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

pairdist.psp | Pairwise distances between line segments | |

plot.fv | Plot Function Values | |

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

plot.linnet | Plot a linear network | |

pairwise.family | Pairwise Interaction Process Family | |

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

opening | Morphological Opening | |

quantile.density | Quantiles of a Density Estimate | |

persp.im | Perspective Plot of Pixel Image | |

print.quad | Print a Quadrature Scheme | |

parameters | Extract Model Parameters in Understandable Form | |

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

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

inforder.family | Infinite Order Interaction Family | |

pairdist.ppp | Pairwise distances | |

is.rectangle | Determine Type of Window | |

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

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

parres | Partial Residuals for Point Process Model | |

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

rpoispp | Generate Poisson Point Pattern | |

rNeymanScott | Simulate Neyman-Scott Process | |

pixellate.owin | Convert Window to Pixel Image | |

layered | Create List of Plotting Layers | |

rLGCP | Simulate Log-Gaussian Cox Process | |

quadratresample | Resample a Point Pattern by Resampling Quadrats | |

rsyst | Simulate systematic random point pattern | |

perimeter | Perimeter Length of Window | |

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

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

plot.hyperframe | Plot Entries in a Hyperframe | |

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

rmhexpand | Specify Simulation Window or Expansion Rule | |

plot.colourmap | Plot a Colour Map | |

rMosaicSet | Mosaic Random Set | |

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

rmpoint | Generate N Random Multitype Points | |

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

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

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

rags | Alternating Gibbs Sampler for Multitype Point Processes | |

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

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

rescue.rectangle | Convert Window Back To Rectangle | |

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

relrisk | Estimate of Spatially-Varying Relative Risk | |

pointsOnLines | Place Points Evenly Along Specified Lines | |

plot.envelope | Plot a Simulation Envelope | |

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

plot.quadratcount | Plot Quadrat Counts | |

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

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

reach | Interaction Distance of a Point Process | |

plot.listof | Plot a List of Things | |

plot.fasp | Plot a Function Array | |

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

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

plot.ppp | plot a Spatial Point Pattern | |

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

ppm | Fit Point Process Model to Data | |

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

pp3 | Three Dimensional Point Pattern | |

plot.profilepl | Plot Profile Likelihood | |

nnwhich | Nearest neighbour | |

plot.pppmatching | Plot a Point Matching | |

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

plot.quad | Plot a Spatial Quadrature Scheme | |

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

rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |

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

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

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

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

methods.funxy | Methods for Spatial Functions | |

rotate.psp | Rotate a Line Segment Pattern | |

linequad | Quadrature Scheme on a Linear Network | |

ppx | Multidimensional Space-Time Point Pattern | |

plot.texturemap | Plot a Texture Map | |

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

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

ppp | Create a Point Pattern | |

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

rPenttinen | Perfect Simulation of the Penttinen Process | |

rmpoispp | Generate Multitype Poisson Point Pattern | |

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

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

pool.fv | Pool Several Functions | |

rotate.infline | Rotate or Shift Infinite Lines | |

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

psp.object | Class of Line Segment Patterns | |

rotate.im | Rotate a Pixel Image | |

rMaternII | Simulate Matern Model II | |

points.lpp | Draw Points on Existing Plot | |

rppm | Recursively Partitioned Point Process Model | |

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

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

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

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

linfun | Function on a Linear Network | |

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

localKinhom | Inhomogeneous Neighbourhood Density Function | |

polartess | Tessellation Using Polar Coordinates | |

rMosaicField | Mosaic Random Field | |

range.fv | Range of Function Values | |

quadrats | Divide Region into Quadrats | |

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

markmarkscatter | Mark-Mark Scatter Plot | |

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

rcell | Simulate Baddeley-Silverman Cell Process | |

project2set | Find Nearest Point in a Region | |

matrixpower | Power of a Matrix | |

rStrauss | Perfect Simulation of the Strauss Process | |

rshift | Random Shift | |

pool | Pool Data | |

scan.test | Spatial Scan Test | |

project2segment | Move Point To Nearest Line | |

model.images | Compute Images of Constructed Covariates | |

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

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

marks | Marks of a Point Pattern | |

sdrPredict | Compute Predictors from Sufficient Dimension Reduction | |

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

nnorient | Nearest Neighbour Orientation Distribution | |

sdr | Sufficient Dimension Reduction | |

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

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

methods.linnet | Methods for Linear Networks | |

multiplicity.ppp | Count Multiplicity of Duplicate Points | |

polynom | Polynomial in One or Two Variables | |

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

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

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

pairorient | Point Pair Orientation Distribution | |

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

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

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

scaletointerval | Rescale Data to Lie Between Specified Limits | |

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

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

rex | Richardson Extrapolation | |

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

progressreport | Print Progress Reports | |

rectcontact | Contact Distribution Function using Rectangular Structuring Element | |

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

quantess | Quantile Tessellation | |

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

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

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

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

rPoissonCluster | Simulate Poisson Cluster Process | |

rcelllpp | Simulate Cell Process on Linear Network | |

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

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

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

nvertices | Count Number of Vertices | |

pairdist.default | Pairwise distances | |

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

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

rMatClust | Simulate Matern Cluster Process | |

pairs.im | Scatterplot Matrix for Pixel Images | |

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

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

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

plot.im | Plot a Pixel Image | |

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

rGaussPoisson | Simulate Gauss-Poisson Process | |

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

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

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

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

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

reflect | Reflect In Origin | |

rjitter | Random Perturbation of a Point Pattern | |

ripras | Estimate window from points alone | |

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

runiflpp | Uniform Random Points on a Linear Network | |

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

rThomas | Simulate Thomas Process | |

rescale | Convert dataset to another unit of length | |

rgbim | Create Colour-Valued Pixel Image | |

rotate | Rotate | |

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

pixelquad | Quadrature Scheme Based on Pixel Grid | |

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

rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |

rMaternI | Simulate Matern Model I | |

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

plot.anylist | Plot a List of Things | |

psib | Sibling Probability of Cluster Point Process | |

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

regularpolygon | Create A Regular Polygon | |

repairNetwork | Repair Internal Data in a Linear Network | |

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

rat | Ratio object | |

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

rose | Rose Diagram | |

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

rdpp | Simulation of a Determinantal Point Process | |

rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |

plot.owin | Plot a Spatial Window | |

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

quasirandom | Quasirandom Patterns | |

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

plot.influence.ppm | Plot Influence Measure | |

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

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

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

plot.imlist | Plot a List of Images | |

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

scalardilate | Apply Scalar Dilation | |

rotate.owin | Rotate a Window | |

rpoislpp | Poisson Point Process on a Linear Network | |

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

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

rshift.ppp | Randomly Shift a Point Pattern | |

rotate.ppp | Rotate a Point Pattern | |

plot.symbolmap | Plot a Graphics Symbol Map | |

rknn | Theoretical Distribution of Nearest Neighbour Distance | |

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

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

rlpp | Random Points on a Linear Network | |

rlabel | Random Re-Labelling of Point Pattern | |

rstrat | Simulate Stratified Random Point Pattern | |

shift.owin | Apply Vector Translation To Window | |

rotmean | Rotational Average of a Pixel Image | |

pool.quadrattest | Pool Several Quadrat Tests | |

rpoislinetess | Poisson Line Tessellation | |

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

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

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

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

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

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

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

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

psp | Create a Line Segment Pattern | |

plot.tess | Plot a Tessellation | |

rounding | Detect Numerical Rounding | |

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

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

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

pppmatching.object | Class of Point Matchings | |

shift | Apply Vector Translation | |

spatdim | Spatial Dimension of a Dataset | |

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

scanpp | Read Point Pattern From Data File | |

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

pppmatching | Create a Point Matching | |

runifpoint | Generate N Uniform Random Points | |

scanLRTS | Likelihood Ratio Test Statistic for Scan Test | |

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

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

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

quantile.im | Sample Quantiles of Pixel Image | |

solist | List of Two-Dimensional Spatial Objects | |

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

rSSI | Simulate Simple Sequential Inhibition | |

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

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

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

quad.object | Class of Quadrature Schemes | |

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

rectdistmap | Distance Map Using Rectangular Distance Metric | |

spatialcdf | Spatial Cumulative Distribution Function | |

rnoise | Random Pixel Noise | |

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

requireversion | Require a Specific Version of a Package | |

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

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

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

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

slrm | Spatial Logistic Regression | |

rQuasi | Generate Quasirandom Point Pattern in Given Window | |

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

rpoint | Generate N Random Points | |

rthin | Random Thinning | |

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

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

setcov | Set Covariance of a Window | |

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

rthinclumps | Random Thinning of Clumps | |

roc | Receiver Operating Characteristic | |

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

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

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

sharpen | Data Sharpening of Point Pattern | |

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

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

rpoisline | Generate Poisson Random Line Process | |

No Results! |

## Vignettes of spatstat

Name | ||

bugfixes.Rnw | ||

datasets.Rnw | ||

getstart.Rnw | ||

hexagon.eps | ||

hexagon.pdf | ||

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## Last month downloads

## Details

Date | 2020-02-22 |

License | GPL (>= 2) |

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

LazyData | true |

NeedsCompilation | yes |

ByteCompile | true |

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

Packaged | 2020-02-22 08:14:02 UTC; adrian |

Repository | CRAN |

Date/Publication | 2020-02-23 17:40:02 UTC |

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

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

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

Contributors | Adrian Baddeley, Rolf Turner, Ege Rubak |

#### Include our badge in your README

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```