# spatstat v1.61-0

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

## Functions in spatstat

Name | Description | |

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

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

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

CDF | Cumulative Distribution Function From Kernel Density Estimate | |

Concom | The Connected Component Process Model | |

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

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

Emark | Diagnostics for random marking | |

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

Fiksel | The Fiksel Interaction | |

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

AreaInter | The Area Interaction Point Process Model | |

BadGey | Hybrid Geyer Point Process Model | |

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

Extract.linnet | Extract Subset of Linear Network | |

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

Extract.owin | Extract Subset of Window | |

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

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

DiggleGratton | Diggle-Gratton model | |

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

Gest | Nearest Neighbour Distance Function G | |

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

[.ssf | Subset of spatially sampled function | |

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

HierHard | The Hierarchical Hard Core Point Process Model | |

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

Extract.quad | Subset of Quadrature Scheme | |

Gcom | Model Compensator of Nearest Neighbour Function | |

Finhom | Inhomogeneous Empty Space Function | |

FmultiInhom | Inhomogeneous Marked F-Function | |

HierStrauss | The Hierarchical Strauss Point Process Model | |

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

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

Iest | Estimate the I-function | |

Jinhom | Inhomogeneous J-function | |

Extract.im | Extract Subset of Image | |

Jmulti | Marked J Function | |

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

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

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

Geyer | Geyer's Saturation Point Process Model | |

Hardcore | The Hard Core Point Process Model | |

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

Kdot.inhom | Inhomogeneous Multitype K Dot Function | |

Hest | Spherical Contact Distribution Function | |

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

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

Kmulti | Marked K-Function | |

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

Hybrid | Hybrid Interaction Point Process Model | |

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

GmultiInhom | Inhomogeneous Marked G-Function | |

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

Gres | Residual G Function | |

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

Gfox | Foxall's Distance Functions | |

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

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

Smooth.ssf | Smooth a Spatially Sampled Function | |

Lest | L-function | |

Kmeasure | Reduced Second Moment Measure | |

Linhom | L-function | |

Ops.msr | Arithmetic Operations on Measures | |

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

Kcom | Model Compensator of K Function | |

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

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

Kest | K-function | |

Kscaled | Locally Scaled K-function | |

Ksector | Sector K-function | |

Ord | Generic Ord Interaction model | |

WindowOnly | Extract Window of Spatial Object | |

Kest.fft | K-function using FFT | |

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

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

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

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

Strauss | The Strauss Point Process Model | |

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

Smooth.fv | Apply Smoothing to Function Values | |

affine.owin | Apply Affine Transformation To Window | |

Kcross.inhom | Inhomogeneous Cross K Function | |

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

OrdThresh | Ord's Interaction model | |

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

LambertW | Lambert's W Function | |

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

Triplets | The Triplet Point Process Model | |

Tstat | Third order summary statistic | |

Ldot.inhom | Inhomogeneous Multitype L Dot Function | |

MinkowskiSum | Minkowski Sum of Windows | |

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

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

MultiHard | The Multitype Hard Core Point Process Model | |

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

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

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

append.psp | Combine Two Line Segment Patterns | |

angles.psp | Orientation Angles of Line Segments | |

LennardJones | The Lennard-Jones Potential | |

Saturated | Saturated Pairwise Interaction model | |

MultiStrauss | The Multitype Strauss Point Process Model | |

Smooth | Spatial smoothing of data | |

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

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

add.texture | Fill Plot With Texture | |

clickjoin | Interactively join vertices on a plot | |

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

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

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

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

as.fv | Convert Data To Class fv | |

as.layered | Convert Data To Layered Object | |

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

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

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

as.tess | Convert Data To Tessellation | |

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

affine.tess | Apply Geometrical Transformation To Tessellation | |

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

Gmulti | Marked Nearest Neighbour Distance Function | |

Ginhom | Inhomogeneous Nearest Neighbour Function | |

bdist.pixels | Distance to Boundary of Window | |

area.owin | Area of a Window | |

areaGain | Difference of Disc Areas | |

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

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

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

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

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

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

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

Jest | Estimate the J-function | |

Kinhom | Inhomogeneous K-function | |

Softcore | The Soft Core Point Process Model | |

as.ppm | Extract Fitted Point Process Model | |

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

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

bdist.points | Distance to Boundary of Window | |

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

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

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

bugfixes | List Recent Bug Fixes | |

clarkevans.test | Clark and Evans Test | |

border | Border Region of a Window | |

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

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

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

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

areaLoss | Difference of Disc Areas | |

as.ppp | Convert Data To Class ppp | |

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

bind.fv | Combine Function Value Tables | |

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

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

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

Kmark | Mark-Weighted K Function | |

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

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

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

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

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

anylist | List of Objects | |

branchlabelfun | Tree Branch Membership Labelling Function | |

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

as.hyperframe | Convert Data to Hyperframe | |

Kmulti.inhom | Inhomogeneous Marked K-Function | |

clickdist | Interactively Measure Distance | |

clickppp | Interactively Add Points | |

Kres | Residual K Function | |

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

envelopeArray | Array of Simulation Envelopes of Summary Function | |

edges2triangles | List Triangles in a Graph | |

clusterset | Allard-Fraley Estimator of Cluster Feature | |

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

bc.ppm | Bias Correction for Fitted Model | |

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

auc | Area Under ROC Curve | |

clusterkernel | Extract Cluster Offspring Kernel | |

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

Lcross.inhom | Inhomogeneous Cross Type L Function | |

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

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

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

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

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

dkernel | Kernel distributions and random generation | |

clickbox | Interactively Define a Rectangle | |

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

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

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

compatible | Test Whether Objects Are Compatible | |

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

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

as.psp | Convert Data To Class psp | |

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

concatxy | Concatenate x,y Coordinate Vectors | |

closepairs | Close Pairs of Points | |

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

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

beginner | Print Introduction For Beginners | |

as.rectangle | Window Frame | |

bdist.tiles | Distance to Boundary of Window | |

boxx | Multi-Dimensional Box | |

discpartarea | Area of Part of Disc | |

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

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

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

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

Pairwise | Generic Pairwise Interaction model | |

Penttinen | Penttinen Interaction | |

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

connected | Connected components | |

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

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

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

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

Poisson | Poisson Point Process Model | |

bw.abram | Abramson's Adaptive Bandwidths | |

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

as.owin | Convert Data To Class owin | |

begins | Check Start of Character String | |

convexify | Weil's Convexifying Operation | |

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

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

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

clarkevans | Clark and Evans Aggregation Index | |

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

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

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

colourmap | Colour Lookup Tables | |

distmap.ppp | Distance Map of Point Pattern | |

circdensity | Density Estimation for Circular Data | |

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

convexhull | Convex Hull | |

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

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

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

convolve.im | Convolution of Pixel Images | |

addvar | Added Variable Plot for Point Process Model | |

covering | Cover Region with Discs | |

clusterfield | Field of clusters | |

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

colourtools | Convert and Compare Colours in Different Formats | |

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

affine | Apply Affine Transformation | |

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

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

deltametric | Delta Metric | |

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

dilation | Morphological Dilation | |

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

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

blur | Apply Gaussian Blur to a Pixel Image | |

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

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

compareFit | Residual Diagnostics for Multiple Fitted Models | |

delaunayDistance | Distance on Delaunay Triangulation | |

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

corners | Corners of a rectangle | |

eval.im | Evaluate Expression Involving Pixel Images | |

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

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

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

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

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

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

centroid.owin | Centroid of a window | |

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

distmap.owin | Distance Map of Window | |

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

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

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

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

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

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

complement.owin | Take Complement of a Window | |

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

convexhull.xy | Convex Hull of Points | |

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

dppPowerExp | Power Exponential Spectral Determinantal Point Process Model | |

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

dppBessel | Bessel Type Determinantal Point Process Model | |

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

edit.hyperframe | Invoke Text Editor on Hyperframe | |

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

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

crossdist | Pairwise distances | |

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

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

deriv.fv | Calculate Derivative of Function Values | |

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

edges | Extract Boundary Edges of a Window. | |

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

disc | Circular Window | |

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

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

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

envelope.envelope | Recompute Envelopes | |

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

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

as.im | Convert to Pixel Image | |

dirichletAreas | Compute Areas of Tiles in Dirichlet Tessellation | |

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

dppCauchy | Generalized Cauchy Determinantal Point Process Model | |

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

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

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

delaunay | Delaunay Triangulation of Point Pattern | |

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

distmap | Distance Map | |

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

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

box3 | Three-Dimensional Box | |

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

as.interact | Extract Interaction Structure | |

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

diameter | Diameter of an Object | |

dfbetas.ppm | Parameter Influence Measure | |

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

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

dppapproxkernel | Approximate Determinantal Point Process Kernel | |

dppeigen | Internal function calculating eig and index | |

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

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

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

edges2vees | List Dihedral Triples in a Graph | |

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

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

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

fv | Create a Function Value Table | |

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

distcdf | Distribution Function of Interpoint Distance | |

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

emend | Force Model to be Valid | |

dirichletVertices | Vertices and Edges of Dirichlet Tessellation | |

is.multitype | Test whether Object is Multitype | |

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

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

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

hybrid.family | Hybrid Interaction Family | |

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

dirichletWeights | Compute Quadrature Weights Based on Dirichlet Tessellation | |

eroded.areas | Areas of Morphological Erosions | |

eval.fv | Evaluate Expression Involving Functions | |

intensity | Intensity of a Dataset or a Model | |

fryplot | Fry Plot of Point Pattern | |

intensity.ppp | Empirical Intensity of Point Pattern | |

fasp.object | Function Arrays for Spatial Patterns | |

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

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

discs | Union of Discs | |

domain | Extract the Domain of any Spatial Object | |

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

foo | Foo is Not a Real Name | |

boundingcircle | Smallest Enclosing Circle | |

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

hextess | Hexagonal Grid or Tessellation | |

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

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

hierpair.family | Hierarchical Pairwise Interaction Process Family | |

erosion | Morphological Erosion by a Disc | |

fardist | Farthest Distance to Boundary of Window | |

ewcdf | Weighted Empirical Cumulative Distribution Function | |

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

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

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

pairs.im | Scatterplot Matrix for Pixel Images | |

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

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

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

linim | Create Pixel Image on Linear Network | |

dmixpois | Mixed Poisson Distribution | |

infline | Infinite Straight Lines | |

closing | Morphological Closing | |

clicklpp | Interactively Add Points on a Linear Network | |

contour.imlist | Array of Contour Plots | |

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

clickpoly | Interactively Define a Polygon | |

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

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

edge.Trans | Translation Edge Correction | |

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

integral.msr | Integral of a Measure | |

integral.linim | Integral on a Linear Network | |

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

is.rectangle | Determine Type of Window | |

hopskel | Hopkins-Skellam Test | |

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

closetriples | Close Triples of Points | |

increment.fv | Increments of a Function | |

markconnect | Mark Connection Function | |

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

fourierbasis | Fourier Basis Functions | |

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

harmonic | Basis for Harmonic Functions | |

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

inforder.family | Infinite Order Interaction Family | |

harmonise | Make Objects Compatible | |

interp.colourmap | Interpolate smoothly between specified colours | |

contour.im | Contour plot of pixel image | |

eem | Exponential Energy Marks | |

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

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

lut | Lookup Tables | |

intensity.quadratcount | Intensity Estimates Using Quadrat Counts | |

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

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

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

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

eval.fasp | Evaluate Expression Involving Function Arrays | |

localKinhom | Inhomogeneous Neighbourhood Density Function | |

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

hyperframe | Hyper Data Frame | |

erosionAny | Morphological Erosion of Windows | |

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

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

harmonise.im | Make Pixel Images Compatible | |

fv.object | Function Value Table | |

interp.im | Interpolate a Pixel Image | |

gridweights | Compute Quadrature Weights Based on Grid Counts | |

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

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

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

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

linfun | Function on a Linear Network | |

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

kernel.squint | Integral of Squared Kernel | |

kppm | Fit Cluster or Cox Point Process Model | |

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

laslett | Laslett's Transform | |

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

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

gridcentres | Rectangular grid of points | |

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

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

funxy | Spatial Function Class | |

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

im.object | Class of Images | |

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

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

im | Create a Pixel Image Object | |

imcov | Spatial Covariance of a Pixel Image | |

diameter.owin | Diameter of a Window | |

dppGauss | Gaussian Determinantal Point Process Model | |

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

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

lengths.psp | Lengths of Line Segments | |

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

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

pixellate | Convert Spatial Object to Pixel Image | |

harmonise.fv | Make Function Tables Compatible | |

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

nnorient | Nearest Neighbour Orientation Distribution | |

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

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

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

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

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

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

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

kernel.moment | Moment of Smoothing Kernel | |

lpp | Create Point Pattern on Linear Network | |

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

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

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

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

logLik.slrm | Loglikelihood of Spatial Logistic Regression | |

kernel.factor | Scale factor for density kernel | |

linearpcf | Linear Pair Correlation Function | |

methods.objsurf | Methods for Objective Function Surfaces | |

measureContinuous | Discrete and Continuous Components of a Measure | |

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

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

lixellate | Subdivide Segments of a Network | |

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

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

model.images | Compute Images of Constructed Covariates | |

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

linequad | Quadrature Scheme on a Linear Network | |

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

project2set | Find Nearest Point in a Region | |

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

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

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

dimhat | Estimate Dimension of Central Subspace | |

dilated.areas | Areas of Morphological Dilations | |

dirichlet | Dirichlet Tessellation of Point Pattern | |

distfun | Distance Map as a Function | |

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

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

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

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

linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |

localpcf | Local pair correlation function | |

marks.tess | Marks of a Tessellation | |

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

linnet | Create a Linear Network | |

levelset | Level Set of a Pixel Image | |

plot.leverage.ppm | Plot Leverage Function | |

miplot | Morisita Index Plot | |

dppMatern | Whittle-Matern Determinantal Point Process Model | |

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

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

dppm | Fit Determinantal Point Process Model | |

distfun.lpp | Distance Map on Linear Network | |

incircle | Find Largest Circle Inside Window | |

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

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

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

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

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

lohboot | Bootstrap Confidence Bands for Summary Function | |

owin.object | Class owin | |

methods.funxy | Methods for Spatial Functions | |

pairorient | Point Pair Orientation Distribution | |

nestsplit | Nested Split | |

nearestsegment | Find Line Segment Nearest to Each Point | |

nnwhich | Nearest neighbour | |

nndist | Nearest neighbour distances | |

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

ppm | Fit Point Process Model to Data | |

plot.layered | Layered Plot | |

owin | Create a Window | |

plot.texturemap | Plot a Texture Map | |

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

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

pairwise.family | Pairwise Interaction Process Family | |

lintess | Tessellation on a Linear Network | |

nncross | Nearest Neighbours Between Two Patterns | |

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

plot.colourmap | Plot a Colour Map | |

mincontrast | Method of Minimum Contrast | |

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

pcfmulti | Marked pair correlation function | |

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

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

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

pairdist.ppx | Pairwise Distances in Any Dimensions | |

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

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

plot.textstring | Plot a Text String | |

ord.family | Ord Interaction Process Family | |

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

methods.linnet | Methods for Linear Networks | |

pixelquad | Quadrature Scheme Based on Pixel Grid | |

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

plot.anylist | Plot a List of Things | |

insertVertices | Insert New Vertices in a Linear Network | |

endpoints.psp | Endpoints of Line Segment Pattern | |

overlap.owin | Compute Area of Overlap | |

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

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

intersect.tess | Intersection of Two Tessellations | |

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

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

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

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

ellipse | Elliptical Window. | |

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

lineardirichlet | Dirichlet Tessellation on a Linear Network | |

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

linearKinhom | Inhomogeneous Linear K Function | |

markvario | Mark Variogram | |

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

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

plot.tess | Plot a Tessellation | |

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

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

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

perimeter | Perimeter Length of Window | |

plot.laslett | Plot Laslett Transform | |

reach | Interaction Distance of a Point Process | |

rose | Rose Diagram | |

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

quadrats | Divide Region into Quadrats | |

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

plot.listof | Plot a List of Things | |

lurking | Lurking Variable Plot | |

pool.envelope | Pool Data from Several Envelopes | |

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

methods.unitname | Methods for Units | |

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

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

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

plot.onearrow | Plot an Arrow | |

parres | Partial Residuals for Point Process Model | |

pp3 | Three Dimensional Point Pattern | |

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

rLGCP | Simulate Log-Gaussian Cox Process | |

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

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

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

envelope | Simulation Envelopes of Summary Function | |

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

harmonise.msr | Make Measures Compatible | |

flipxy | Exchange X and Y Coordinates | |

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

expand.owin | Apply Expansion Rule | |

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

psp | Create a Line Segment Pattern | |

layered | Create List of Plotting Layers | |

integral.im | Integral of a Pixel Image | |

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

methods.ssf | Methods for Spatially Sampled Functions | |

points.lpp | Draw Points on Existing Plot | |

markmarkscatter | Mark-Mark Scatter Plot | |

methods.distfun | Geometrical Operations for Distance Functions | |

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

grow.rectangle | Add margins to rectangle | |

harmonise.owin | Make Windows Compatible | |

rStrauss | Perfect Simulation of the Strauss Process | |

npfun | Dummy Function Returns Number of Points | |

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

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

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

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

linearK | Linear K Function | |

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

methods.layered | Methods for Layered Objects | |

rescue.rectangle | Convert Window Back To Rectangle | |

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

ppx | Multidimensional Space-Time Point Pattern | |

pool.fv | Pool Several Functions | |

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

pppdist | Distance Between Two Point Patterns | |

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

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

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

pool.quadrattest | Pool Several Quadrat Tests | |

nnclean | Nearest Neighbour Clutter Removal | |

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

nnwhich.pp3 | Nearest neighbours in three dimensions | |

matchingdist | Distance for a Point Pattern Matching | |

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

methods.fii | Methods for Fitted Interactions | |

plot.fv | Plot Function Values | |

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

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

quadratresample | Resample a Point Pattern by Resampling Quadrats | |

rescale | Convert dataset to another unit of length | |

pppmatching | Create a Point Matching | |

pppmatching.object | Class of Point Matchings | |

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

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

rMatClust | Simulate Matern Cluster Process | |

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

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

pairdist.psp | Pairwise distances between line segments | |

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

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

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

parameters | Extract Model Parameters in Understandable Form | |

rSSI | Simulate Simple Sequential Inhibition | |

rjitter | Random Perturbation of a Point Pattern | |

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

rat | Ratio object | |

psp.object | Class of Line Segment Patterns | |

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

roc | Receiver Operating Characteristic | |

rdpp | Simulation of a Determinantal Point Process | |

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

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

ripras | Estimate window from points alone | |

rectcontact | Contact Distribution Function using Rectangular Structuring Element | |

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

quadratcount | Quadrat counting for a point pattern | |

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

multiplicity.ppp | Count Multiplicity of Duplicate Points | |

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

nvertices | Count Number of Vertices | |

pairdist.default | Pairwise distances | |

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

rotate.owin | Rotate a Window | |

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

is.marked | Test Whether Marks Are Present | |

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

sdrPredict | Compute Predictors from Sufficient Dimension Reduction | |

methods.zclustermodel | Methods for Cluster Models | |

rpoispp | Generate Poisson Point Pattern | |

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

localKcross.inhom | Inhomogeneous Multitype K Function | |

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

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

nnfun | Nearest Neighbour Index Map as a Function | |

plot.imlist | Plot a List of Images | |

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

mergeLevels | Merge Levels of a Factor | |

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

plot.cdftest | Plot a Spatial Distribution Test | |

pcf | Pair Correlation Function | |

joinVertices | Join Vertices in a Network | |

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

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

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

periodify | Make Periodic Copies of a Spatial Pattern | |

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

pixellate.owin | Convert Window to Pixel Image | |

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

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

rnoise | Random Pixel Noise | |

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

quantess | Quantile Tessellation | |

rotate | Rotate | |

rshift.ppp | Randomly Shift a Point Pattern | |

spatstat-internal | Internal spatstat functions | |

scalardilate | Apply Scalar Dilation | |

pixelcentres | Extract Pixel Centres as Point Pattern | |

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

sdr | Sufficient Dimension Reduction | |

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

rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |

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

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

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

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

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

spatstat-deprecated | Deprecated spatstat functions | |

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

persp.im | Perspective Plot of Pixel Image | |

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

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

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

plot.linnet | Plot a linear network | |

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

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

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

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

pointsOnLines | Place Points Evenly Along Specified Lines | |

plot.ssf | Plot a Spatially Sampled Function | |

plot.ppp | plot a Spatial Point Pattern | |

plot.hyperframe | Plot Entries in a Hyperframe | |

plot.pppmatching | Plot a Point Matching | |

polartess | Tessellation Using Polar Coordinates | |

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

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

reflect | Reflect In Origin | |

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

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

npoints | Number of Points in a Point Pattern | |

plot.bermantest | Plot Result of Berman Test | |

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

localK | Neighbourhood density function | |

rotate.ppp | Rotate a Point Pattern | |

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

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

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

plot.im | Plot a Pixel Image | |

print.quad | Print a Quadrature Scheme | |

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

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

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

pairdist | Pairwise distances | |

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

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

padimage | Pad the Border of a Pixel Image | |

rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |

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

rMaternI | Simulate Matern Model I | |

rHardcore | Perfect Simulation of the Hardcore Process | |

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

psib | Sibling Probability of Cluster Point Process | |

rGaussPoisson | Simulate Gauss-Poisson Process | |

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

rMosaicSet | Mosaic Random Set | |

quantile.density | Quantiles of a Density Estimate | |

rthinclumps | Random Thinning of Clumps | |

rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |

runiflpp | Uniform Random Points on a Linear Network | |

runifpoint | Generate N Uniform Random Points | |

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

rthin | Random Thinning | |

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

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

rex | Richardson Extrapolation | |

rMaternII | Simulate Matern Model II | |

scan.test | Spatial Scan Test | |

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

objsurf | Objective Function Surface | |

rstrat | Simulate Stratified Random Point Pattern | |

rags | Alternating Gibbs Sampler for Multitype Point Processes | |

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

msr | Signed or Vector-Valued Measure | |

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

markcorr | Mark Correlation Function | |

regularpolygon | Create A Regular Polygon | |

markcrosscorr | Mark Cross-Correlation Function | |

matrixpower | Power of a Matrix | |

pool | Pool Data | |

pairdist.ppp | Pairwise distances | |

plot.quadratcount | Plot Quadrat Counts | |

plot.symbolmap | Plot a Graphics Symbol Map | |

polynom | Polynomial in One or Two Variables | |

pairdist.pp3 | Pairwise distances in Three Dimensions | |

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

marks | Marks of a Point Pattern | |

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

plot.studpermutest | Plot a Studentised Permutation Test | |

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

rgbim | Create Colour-Valued Pixel Image | |

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

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

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

midpoints.psp | Midpoints of Line Segment Pattern | |

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

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

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

nnmark | Mark of Nearest Neighbour | |

range.fv | Range of Function Values | |

quantile.im | Sample Quantiles of Pixel Image | |

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

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

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

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

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

nnmap | K-th Nearest Point Map | |

shift | Apply Vector Translation | |

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

rMosaicField | Mosaic Random Field | |

quad.object | Class of Quadrature Schemes | |

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

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

solist | List of Two-Dimensional Spatial Objects | |

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

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

rppm | Recursively Partitioned Point Process Model | |

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

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

requireversion | Require a Specific Version of a Package | |

rmpoispp | Generate Multitype Poisson Point Pattern | |

nnwhich.ppx | Nearest Neighbours in Any Dimensions | |

opening | Morphological Opening | |

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

rshift | Random Shift | |

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

rotate.im | Rotate a Pixel Image | |

ppp | Create a Point Pattern | |

rNeymanScott | Simulate Neyman-Scott Process | |

relrisk | Estimate of Spatially-Varying Relative Risk | |

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

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

rlabel | Random Re-Labelling of Point Pattern | |

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

rknn | Theoretical Distribution of Nearest Neighbour Distance | |

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

rotate.infline | Rotate or Shift Infinite Lines | |

rsyst | Simulate systematic random point pattern | |

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

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 | |

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

scaletointerval | Rescale Data to Lie Between Specified Limits | |

slrm | Spatial Logistic Regression | |

rPenttinen | Perfect Simulation of the Penttinen Process | |

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

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

plot.owin | Plot a Spatial Window | |

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

rectdistmap | Distance Map Using Rectangular Distance Metric | |

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

pcfinhom | Inhomogeneous Pair Correlation Function | |

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

rotate.psp | Rotate a Line Segment Pattern | |

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

setcov | Set Covariance of a Window | |

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

rmpoint | Generate N Random Multitype Points | |

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

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

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

plot.influence.ppm | Plot Influence Measure | |

plot.envelope | Plot a Simulation Envelope | |

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

plot.fasp | Plot a Function Array | |

plot.quad | Plot a Spatial Quadrature Scheme | |

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

project2segment | Move Point To Nearest Line | |

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

ppp.object | Class of Point Patterns | |

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

progressreport | Print Progress Reports | |

rThomas | Simulate Thomas Process | |

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

quasirandom | Quasirandom Patterns | |

rPoissonCluster | Simulate Poisson Cluster Process | |

rcell | Simulate Baddeley-Silverman Cell Process | |

rQuasi | Generate Quasirandom Point Pattern in Given Window | |

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

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

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

scanLRTS | Likelihood Ratio Test Statistic for Scan Test | |

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

rmhexpand | Specify Simulation Window or Expansion Rule | |

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

spatdim | Spatial Dimension of a Dataset | |

spatialcdf | Spatial Cumulative Distribution Function | |

rotmean | Rotational Average of a Pixel Image | |

rpoislpp | Poisson Point Process on a Linear Network | |

scanpp | Read Point Pattern From Data File | |

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

rpoislinetess | Poisson Line Tessellation | |

sharpen | Data Sharpening of Point Pattern | |

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

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

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

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

rpoisline | Generate Poisson Random Line Process | |

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

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

rlpp | Random Points on a Linear Network | |

rpoint | Generate N Random Points | |

rounding | Detect Numerical Rounding | |

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

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

shift.owin | Apply Vector Translation To Window | |

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

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 | |

No Results! |

## Vignettes of spatstat

Name | ||

bugfixes.Rnw | ||

datasets.Rnw | ||

getstart.Rnw | ||

hexagon.eps | ||

hexagon.pdf | ||

irregpoly.eps | ||

irregpoly.pdf | ||

replicated.Rnw | ||

shapefiles.Rnw | ||

updates.Rnw | ||

No Results! |

## Last month downloads

## Details

Date | 2019-09-12 |

License | GPL (>= 2) |

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

LazyData | true |

NeedsCompilation | yes |

ByteCompile | true |

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

Packaged | 2019-09-12 07:51:49 UTC; adrian |

Repository | CRAN |

Date/Publication | 2019-09-22 18:20:03 UTC |

imports | abind , deldir (>= 0.0-21) , goftest , Matrix , mgcv , polyclip (>= 1.10-0) , spatstat.utils (>= 1.13-0) , tensor |

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

depends | graphics , grDevices , methods , nlme , R (>= 3.3.0) , rpart , spatstat.data (>= 1.4-0) , stats , utils |

Contributors | Adrian Baddeley, Rolf Turner, Ege Rubak |

#### Include our badge in your README

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```