# spatstat v1.44-1

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

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

## Functions in spatstat

Name | Description | |

plot.layered | Layered Plot | |

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

pppmatching.object | Class of Point Matchings | |

pool.envelope | Pool Data from Several Envelopes | |

quad.object | Class of Quadrature Schemes | |

runiflpp | Uniform Random Points on a Linear Network | |

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

quadratcount | Quadrat counting for a point pattern | |

quantess | Quantile Tessellation | |

pool.quadrattest | Pool Several Quadrat Tests | |

plot.hyperframe | Plot Entries in a Hyperframe | |

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

plot.leverage.ppm | Plot Leverage Function | |

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

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

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

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

pppmatching | Create a Point Matching | |

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

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

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

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

plot.texturemap | Plot a Texture Map | |

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

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

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

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

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

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

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

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

quasirandom | Quasirandom Patterns | |

profilepl | Profile Maximum Pseudolikelihood or AIC | |

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

pyramidal | Pyramidal Neurons in Cingulate Cortex | |

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

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

ppp | Create a Point Pattern | |

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

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

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

stienen | Stienen Diagram | |

rPoissonCluster | Simulate Poisson Cluster Process | |

rcell | Simulate Baddeley-Silverman Cell Process | |

relrisk | Estimate of Spatially-Varying Relative Risk | |

plot.textstring | Plot a Text String | |

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

plot.tess | Plot a tessellation | |

rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |

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

rLGCP | Simulate Log-Gaussian Cox Process | |

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

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

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

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

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

rgbim | Create Colour-Valued Pixel Image | |

rknn | Theoretical Distribution of Nearest Neighbour Distance | |

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

rHardcore | Perfect Simulation of the Hardcore Process | |

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

psp | Create a Line Segment Pattern | |

quadratresample | Resample a Point Pattern by Resampling Quadrats | |

rmpoint | Generate N Random Multitype Points | |

plot.onearrow | Plot an Arrow | |

rdpp | Simulation of a Determinantal Point Process | |

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

shapley | Galaxies in the Shapley Supercluster | |

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

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

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

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

vertices | Vertices of a Window | |

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

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

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

rpoislpp | Poisson Point Process on a Linear Network | |

rotate | Rotate | |

rotate.ppp | Rotate a Point Pattern | |

pool | Pool Data | |

rotmean | Rotational Average of a Pixel Image | |

slrm | Spatial Logistic Regression | |

rMaternI | Simulate Matern Model I | |

rotate.owin | Rotate a Window | |

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

zclustermodel | Cluster Point Process Model | |

progressreport | Print Progress Reports | |

print.quad | Print a Quadrature Scheme | |

rNeymanScott | Simulate Neyman-Scott Process | |

spatialcdf | Spatial Cumulative Distribution Function | |

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

rmhexpand | Specify Simulation Window or Expansion Rule | |

ppp.object | Class of Point Patterns | |

rMosaicField | Mosaic Random Field | |

rpoisline | Generate Poisson Random Line Process | |

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

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

rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |

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

zapsmall.im | Rounding of Pixel Values | |

plot.owin | Plot a Spatial Window | |

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

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

rstrat | Simulate Stratified Random Point Pattern | |

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

suffstat | Sufficient Statistic of Point Process Model | |

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

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

rThomas | Simulate Thomas Process | |

plot.symbolmap | Plot a Graphics Symbol Map | |

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

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

rpoislinetess | Poisson Line Tessellation | |

rsyst | Simulate systematic random point pattern | |

rescue.rectangle | Convert Window Back To Rectangle | |

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

square | Square Window | |

solist | List of Two-Dimensional Spatial Objects | |

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

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

rshift | Random Shift | |

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

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

plot.fv | Plot Function Values | |

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

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

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

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

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

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

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

summary.quad | Summarizing a Quadrature Scheme | |

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

plot.influence.ppm | Plot Influence Measure | |

plot.fasp | Plot a Function Array | |

scalardilate | Apply Scalar Dilation | |

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

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

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

plot.ppp | plot a Spatial Point Pattern | |

studpermu.test | Studentised Permutation Test | |

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

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

unmark | Remove Marks | |

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

plot.imlist | Plot a List of Images | |

plot.linnet | Plot a linear network | |

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

ponderosa | Ponderosa Pine Tree Point Pattern | |

rmpoispp | Generate Multitype Poisson Point Pattern | |

quadrats | Divide Region into Quadrats | |

rose | Rose Diagram | |

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

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

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

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

setcov | Set Covariance of a Window | |

sumouter | Compute Quadratic Forms | |

shift | Apply Vector Translation | |

swedishpines | Swedish Pines Point Pattern | |

subfits | Extract List of Individual Point Process Models | |

simplenet | Simple Example of Linear Network | |

quantile.density | Quantiles of a Density Estimate | |

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

summary.im | Summarizing a Pixel Image | |

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

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

rounding | Detect Numerical Rounding | |

summary.owin | Summary of a Spatial Window | |

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

treeprune | Prune Tree to Given Level | |

varblock | Estimate Variance of Summary Statistic by Subdivision | |

plot.im | Plot a Pixel Image | |

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

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

pool.fv | Pool Several Functions | |

rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |

textureplot | Plot Image or Tessellation Using Texture Fill | |

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

rMaternII | Simulate Matern Model II | |

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

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

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

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

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

scaletointerval | Rescale Data to Lie Between Specified Limits | |

rjitter | Random Perturbation of a Point Pattern | |

spatstat-deprecated | Deprecated spatstat functions | |

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

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

rPenttinen | Perfect Simulation of the Penttinen Process | |

sporophores | Sporophores Data | |

reflect | Reflect In Origin | |

volume | Volume of an Object | |

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

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

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

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

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

triangulate.owin | Decompose Window into Triangles | |

rQuasi | Generate Quasirandom Point Pattern in Given Window | |

unitname | Name for Unit of Length | |

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

rotate.psp | Rotate a Line Segment Pattern | |

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

rshift.ppp | Randomly Shift a Point Pattern | |

rpoint | Generate N Random Points | |

varcount | Predicted Variance of the Number of Points | |

pp3 | Three Dimensional Point Pattern | |

spatdim | Spatial Dimension of a Dataset | |

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

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

whist | Weighted Histogram | |

tess | Create a Tessellation | |

rthin | Random Thinning | |

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

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

plot.quad | Plot a Spatial Quadrature Scheme | |

transect.im | Pixel Values Along a Transect | |

tilenames | Names of Tiles in a Tessellation | |

shift.owin | Apply Vector Translation To Window | |

scanLRTS | Likelihood Ratio Test Statistic for Scan Test | |

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

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

tiles | Extract List of Tiles in a Tessellation | |

stieltjes | Compute Integral of Function Against Cumulative Distribution | |

quantile.im | Sample Quantiles of Pixel Image | |

rMatClust | Simulate Matern Cluster Process | |

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

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

texturemap | Texture Map | |

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

rStrauss | Perfect Simulation of the Strauss Process | |

pppdist | Distance Between Two Point Patterns | |

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

range.fv | Range of Function Values | |

ppm | Fit Point Process Model to Data | |

rSSI | Simulate Simple Sequential Inhibition | |

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

rescale | Convert dataset to another unit of length | |

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

rlabel | Random Re-Labelling of Point Pattern | |

rotate.im | Rotate a Pixel Image | |

sharpen | Data Sharpening of Point Pattern | |

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

stratrand | Stratified random point pattern | |

timed | Record the Computation Time | |

vesicles | Vesicles Data | |

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

waka | Trees in Waka national park | |

symbolmap | Graphics Symbol Map | |

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

pointsOnLines | Place Points Evenly Along Specified Lines | |

rMosaicSet | Mosaic Random Set | |

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

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

rat | Ratio object | |

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

roc | Receiver Operating Characteristic | |

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

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

scan.test | Spatial Scan Test | |

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

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

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

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

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

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

rnoise | Random Pixel Noise | |

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

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

spatstat-internal | Internal spatstat functions | |

spatstat.options | Internal Options in Spatstat Package | |

ripras | Estimate window from points alone | |

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

runifpoint | Generate N Uniform Random Points | |

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

simdat | Simulated Point Pattern | |

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

spatstat-package | The Spatstat Package | |

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

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

ppx | Multidimensional Space-Time Point Pattern | |

transmat | Convert Pixel Array Between Different Conventions | |

tileindex | Determine Which Tile Contains Each Given Point | |

will.expand | Test Expansion Rule | |

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

project2set | Find Nearest Point in a Region | |

project2segment | Move Point To Nearest Line | |

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

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

Hest | Spherical Contact Distribution Function | |

Extract.owin | Extract Subset of Window | |

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

Extract.quad | Subset of Quadrature Scheme | |

Gfox | Foxall's Distance Functions | |

Kdot.inhom | Inhomogeneous Multitype K Dot Function | |

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

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

Poisson | Poisson Point Process Model | |

rGaussPoisson | Simulate Gauss-Poisson Process | |

Kmulti | Marked K-Function | |

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

reach | Interaction Distance of a Point Process | |

OrdThresh | Ord's Interaction model | |

Linhom | L-function | |

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

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

Kovesi | Colour Sequences with Uniform Perceptual Contrast | |

DiggleGratton | Diggle-Gratton model | |

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

Kmulti.inhom | Inhomogeneous Marked K-Function | |

LennardJones | The Lennard-Jones Potential | |

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

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

BadGey | Hybrid Geyer Point Process Model | |

Lcross.inhom | Inhomogeneous Cross Type L Function | |

Strauss | The Strauss Point Process Model | |

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

bind.fv | Combine Function Value Tables | |

Finhom | Inhomogeneous Empty Space Function | |

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

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

Iest | Estimate the I-function | |

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

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

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

border | Border Region of a Window | |

Ord | Generic Ord Interaction model | |

Kinhom | Inhomogeneous K-function | |

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

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

corners | Corners of a rectangle | |

MultiHard | The Multitype Hard Core Point Process Model | |

add.texture | Fill Plot With Texture | |

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

clusterfield | Field of clusters | |

LambertW | Lambert's W Function | |

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

austates | Australian States and Mainland Territories | |

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

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

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

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

Fiksel | The Fiksel Interaction | |

Saturated | Saturated Pairwise Interaction model | |

dppapproxkernel | Approximate Determinantal Point Process Kernel | |

Hardcore | The Hard Core Point Process Model | |

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

as.im | Convert to Pixel Image | |

Extract.linnet | Extract Subset of Linear Network | |

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

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

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

scanpp | Read Point Pattern From Data File | |

Extract.im | Extract Subset of Image | |

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

Kmeasure | Reduced Second Moment Measure | |

Ldot.inhom | Inhomogeneous Multitype L Dot Function | |

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

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

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

Kcross.inhom | Inhomogeneous Cross K Function | |

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

demopat | Artificial Data Point Pattern | |

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

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

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

Kres | Residual K Function | |

deriv.fv | Calculate Derivative of Function Values | |

Triplets | The Triplet Point Process Model | |

beginner | Print Introduction For Beginners | |

MultiStrauss | The Multitype Strauss Point Process Model | |

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

Jinhom | Inhomogeneous J-function | |

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

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

as.hyperframe | Convert Data to Hyperframe | |

convexhull.xy | Convex Hull of Points | |

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

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

as.psp | Convert Data To Class psp | |

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

as.layered | Convert Data To Layered Object | |

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

Smooth.fv | Apply Smoothing to Function Values | |

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

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

blur | Apply Gaussian Blur to a Pixel Image | |

as.tess | Convert Data To Tessellation | |

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

localK | Neighbourhood density function | |

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

Kest.fft | K-function using FFT | |

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

closing | Morphological Closing | |

affine.tess | Apply Geometrical Transformation To Tessellation | |

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

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

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

branchlabelfun | Tree Branch Membership Labelling Function | |

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

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

Concom | The Connected Component Process Model | |

WindowOnly | Extract Window of Spatial Object | |

contour.imlist | Array of Contour Plots | |

increment.fv | Increments of a Function | |

CDF | Cumulative Distribution Function From Kernel Density Estimate | |

convexhull | Convex Hull | |

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

spokes | Spokes pattern of dummy points | |

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

edit.hyperframe | Invoke Text Editor on Hyperframe | |

betacells | Beta Ganglion Cells in Cat Retina | |

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

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

as.interact | Extract Interaction Structure | |

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

dkernel | Kernel distributions and random generation | |

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

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

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

boxx | Multi-Dimensional Box | |

Smooth | Spatial smoothing of data | |

centroid.owin | Centroid of a window | |

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

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

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

mergeLevels | Merge Levels of a Factor | |

as.ppp | Convert Data To Class ppp | |

bdist.points | Distance to Boundary of Window | |

clickppp | Interactively Add Points | |

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

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

Hybrid | Hybrid Interaction Point Process Model | |

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

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

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

connected.ppp | Connected components of a point pattern | |

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

begins | Check Start of Character String | |

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

dirichletWeights | Compute Quadrature Weights Based on Dirichlet Tessellation | |

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

harmonise.fv | Make Function Tables Compatible | |

concatxy | Concatenate x,y Coordinate Vectors | |

envelope.envelope | Recompute Envelopes | |

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

circdensity | Density Estimation for Circular Data | |

areaGain | Difference of Disc Areas | |

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

dppeigen | Internal function calculating eig and index | |

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

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

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

bdist.tiles | Distance to Boundary of Window | |

gridcentres | Rectangular grid of points | |

linnet | Create a Linear Network | |

Ksector | Sector K-function | |

murchison | Murchison gold deposits | |

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

integral.im | Integral of a Pixel Image | |

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

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

circumradius | Circumradius of a Window | |

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

chicago | Chicago Street Crime Data | |

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

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

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

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

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

spruces | Spruces Point Pattern | |

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

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

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

Gmulti | Marked Nearest Neighbour Distance Function | |

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

deltametric | Delta Metric | |

edge.Trans | Translation Edge Correction | |

flipxy | Exchange X and Y Coordinates | |

distfun.lpp | Distance Map on Linear Network | |

area.owin | Area of a Window | |

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

gridweights | Compute Quadrature Weights Based on Grid Counts | |

superimpose | Superimpose Several Geometric Patterns | |

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

box3 | Three-Dimensional Box | |

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

copyExampleFiles | Copy Data Files for Example | |

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

discpartarea | Area of Part of Disc | |

demohyper | Demonstration Example of Hyperframe of Spatial Data | |

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

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

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

Kscaled | Locally Scaled K-function | |

valid | Check Whether Point Process Model is Valid | |

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

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

hamster | Aherne's hamster tumour data | |

pairs.im | Scatterplot Matrix for Pixel Images | |

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

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

eem | Exponential Energy Marks | |

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

Kest | K-function | |

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

npfun | Dummy Function Returns Number of Points | |

colourmap | Colour Lookup Tables | |

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

methods.linnet | Methods for Linear Networks | |

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

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

Ginhom | Inhomogeneous Nearest Neighbour Function | |

localKinhom | Inhomogeneous Neighbourhood Density Function | |

erosion | Morphological Erosion | |

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

grow.rectangle | Add margins to rectangle | |

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

fasp.object | Function Arrays for Spatial Patterns | |

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

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

contour.im | Contour plot of pixel image | |

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

dppBessel | Bessel Type Determinantal Point Process Model | |

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

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

amacrine | Hughes' Amacrine Cell Data | |

linearKinhom | Inhomogeneous Linear K Function | |

kppm | Fit Cluster or Cox Point Process Model | |

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

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

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

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

dppPowerExp | Power Exponential Spectral Determinantal Point Process Model | |

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

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

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

unnormdensity | Weighted kernel smoother | |

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

intensity | Intensity of a Dataset or a Model | |

dendrite | Dendritic Spines Data | |

dmixpois | Mixed Poisson Distribution | |

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

inforder.family | Infinite Order Interaction Family | |

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

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

edges2vees | List Dihedral Triples in a Graph | |

eval.fasp | Evaluate Expression Involving Function Arrays | |

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

clickdist | Interactively Measure Distance | |

linearK | Linear K Function | |

clarkevans | Clark and Evans Aggregation Index | |

kernel.factor | Scale factor for density kernel | |

as.rectangle | Window Frame | |

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

auc | Area Under ROC Curve | |

nnmap | K-th Nearest Point Map | |

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

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

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

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

funxy | Spatial Function Class | |

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

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

convolve.im | Convolution of Pixel Images | |

ewcdf | Weighted Empirical Cumulative Distribution Function | |

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

envelope | Simulation Envelopes of Summary Function | |

hopskel | Hopkins-Skellam Test | |

HierStrauss | The Hierarchical Strauss Point Process Model | |

clusterset | Allard-Fraley Estimator of Cluster Feature | |

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

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

multiplicity.ppp | Count Multiplicity of Duplicate Points | |

areaLoss | Difference of Disc Areas | |

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

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

ants | Harkness-Isham ants' nests data | |

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

emend | Force Model to be Valid | |

edges2triangles | List Triangles in a Graph | |

hextess | Hexagonal Grid or Tessellation | |

distcdf | Distribution Function of Interpoint Distance | |

distfun | Distance Map as a Function | |

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

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

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

gordon | People in Gordon Square | |

eval.im | Evaluate Expression Involving Pixel Images | |

anylist | List of Objects | |

hyperframe | Hyper Data Frame | |

im.object | Class of Images | |

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

markcrosscorr | Mark Cross-Correlation Function | |

gorillas | Gorilla Nesting Sites | |

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

Extract.fasp | Extract Subset of Function Array | |

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

lpp | Create Point Pattern on Linear Network | |

levelset | Level Set of a Pixel Image | |

flu | Influenza Virus Proteins | |

dirichlet | Dirichlet Tessellation of Point Pattern | |

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

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

mincontrast | Method of Minimum Contrast | |

clickbox | Interactively Define a Rectangle | |

interp.colourmap | Interpolate smoothly between specified colours | |

edges | Extract Boundary Edges of a Window. | |

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

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

pairwise.family | Pairwise Interaction Process Family | |

linim | Create Pixel Image on Linear Network | |

Pairwise | Generic Pairwise Interaction model | |

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

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

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

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

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

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

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

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

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

nearestsegment | Find Line Segment Nearest to Each Point | |

chorley | Chorley-Ribble Cancer Data | |

pixelquad | Quadrature Scheme Based on Pixel Grid | |

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

nnwhich.pp3 | Nearest neighbours in three dimensions | |

Gest | Nearest Neighbour Distance Function G | |

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

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

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

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

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

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

bramblecanes | Hutchings' Bramble Canes data | |

lansing | Lansing Woods Point Pattern | |

compatible | Test Whether Objects Are Compatible | |

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

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

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

pairorient | Point Pair Orientation Distribution | |

complement.owin | Take Complement of a Window | |

dppCauchy | Generalized Cauchy Determinantal Point Process Model | |

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

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

Emark | Diagnostics for random marking | |

matchingdist | Distance for a Point Pattern Matching | |

miplot | Morisita Index Plot | |

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

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

clarkevans.test | Clark and Evans Test | |

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

lurking | Lurking variable plot | |

pairdist | Pairwise distances | |

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

plot.quadratcount | Plot Quadrat Counts | |

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

Penttinen | Penttinen Interaction | |

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

closepairs | Close Pairs of Points | |

nestsplit | Nested Split | |

bdist.pixels | Distance to Boundary of Window | |

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

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

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

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

bronzefilter | Bronze gradient filter data | |

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

Kcom | Model Compensator of K Function | |

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

pcfinhom | Inhomogeneous Pair Correlation Function | |

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

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

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

domain | Extract the Domain of any Spatial Object | |

eroded.areas | Areas of Morphological Erosions | |

nnwhich | Nearest neighbour | |

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

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

pixellate.owin | Convert Window to Pixel Image | |

layered | Create List of Plotting Layers | |

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

model.images | Compute Images of Constructed Covariates | |

owin | Create a Window | |

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

paracou | Kimboto trees at Paracou, French Guiana | |

clickpoly | Interactively Define a Polygon | |

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

moribund | Outdated Functions | |

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

dfbetas.ppm | Parameter influence measure | |

fardist | Farthest Distance to Boundary of Window | |

longleaf | Longleaf Pines Point Pattern | |

nztrees | New Zealand Trees Point Pattern | |

markcorr | Mark Correlation Function | |

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

clmfires | Castilla-La Mancha Forest Fires | |

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

rpoispp | Generate Poisson Point Pattern | |

dppMatern | Whittle-Matern Determinantal Point Process Model | |

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

fv | Create a Function Value Table | |

nnclean | Nearest Neighbour Clutter Removal | |

disc | Circular Window | |

methods.layered | Methods for Layered Objects | |

interp.im | Interpolate a Pixel Image | |

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

as.fv | Convert Data To Class fv | |

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

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

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

fv.object | Function Value Table | |

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

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

connected | Connected components | |

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

dilated.areas | Areas of Morphological Dilations | |

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

append.psp | Combine Two Line Segment Patterns | |

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

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

fourierbasis | Fourier Basis Functions | |

linearpcf | Linear Pair Correlation Function | |

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

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

foo | Foo is Not a Real Name | |

nnmark | Mark of Nearest Neighbour | |

plot.colourmap | Plot a Colour Map | |

convexify | Weil's Convexifying Operation | |

linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |

intensity.ppp | Empirical Intensity of Point Pattern | |

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

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

Kmark | Mark-Weighted K Function | |

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

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

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

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

pcf | Pair Correlation Function | |

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

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

nncross | Nearest Neighbours Between Two Patterns | |

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

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

harmonise.owin | Make Windows Compatible | |

padimage | Pad the Border of a Pixel Image | |

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

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

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

bdspots | Breakdown Spots in Microelectronic Materials | |

cells | Biological Cells Point Pattern | |

plot.anylist | Plot a List of Things | |

dppGauss | Gaussian Determinantal Point Process Model | |

diameter | Diameter of an Object | |

npoints | Number of Points in a Point Pattern | |

diameter.linnet | Circumradius and Diameter of a Linear Network | |

msr | Signed or Vector-Valued Measure | |

crossdist | Pairwise distances | |

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

parres | Partial Residuals for Point Process Model | |

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

japanesepines | Japanese Pines Point Pattern | |

distmap | Distance Map | |

HierHard | The Hierarchical Hard Core Point Process Model | |

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

finpines | Pine saplings in Finland. | |

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

harmonise | Make Objects Compatible | |

humberside | Humberside Data on Childhood Leukaemia and Lymphoma | |

as.ppm | Extract Fitted Point Process Model | |

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

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

harmonise.im | Make Pixel Images Compatible | |

objsurf | Objective Function Surface | |

distmap.ppp | Distance Map of Point Pattern | |

pairdist.default | Pairwise distances | |

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

hierpair.family | Hierarchical Pairwise Interaction Process Family | |

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

clickjoin | Interactively join vertices on a plot | |

nbfires | Point Patterns of New Brunswick Forest Fires | |

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

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

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

integral.linim | Integral on a Linear Network | |

discs | Union of Discs | |

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

logLik.slrm | Loglikelihood of Spatial Logistic Regression | |

delaunayDistance | Distance on Delaunay Triangulation | |

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

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

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

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

letterR | Window in Shape of Letter R | |

delaunay | Delaunay Triangulation of Point Pattern | |

nndist | Nearest neighbour distances | |

opening | Morphological Opening | |

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

incircle | Find Largest Circle Inside Window | |

methods.zclustermodel | Methods for Cluster Models | |

methods.objsurf | Methods for Objective Function Surfaces | |

clusterkernel | Extract Cluster Offspring Kernel | |

methods.funxy | Methods for Spatial Functions | |

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

is.multitype | Test whether Object is Multitype | |

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

pairdist.ppp | Pairwise distances | |

is.marked | Test Whether Marks Are Present | |

copper | Berman-Huntington points and lines data | |

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

psp.object | Class of Line Segment Patterns | |

intensity.quadratcount | Intensity Estimates Using Quadrat Counts | |

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

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

eval.fv | Evaluate Expression Involving Functions | |

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

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

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

expand.owin | Apply Expansion Rule | |

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

hyytiala | Scots pines and other trees at Hyytiala | |

nnfun | Nearest Neighbour Index Map as a Function | |

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

imcov | Spatial Covariance of a Pixel Image | |

dppm | Fit Determinantal Point Process Model | |

localpcf | Local pair correlation function | |

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

infline | Infinite Straight Lines | |

overlap.owin | Compute Area of Overlap | |

linfun | Function on a Linear Network | |

dirichletAreas | Compute Areas of Tiles in Dirichlet Tessellation | |

ord.family | Ord Interaction Process Family | |

anemones | Beadlet Anemones Data | |

mucosa | Cells in Gastric Mucosa | |

endpoints.psp | Endpoints of Line Segment Pattern | |

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

Lest | L-function | |

lixellate | Subdivide Segments of a Network | |

dilation | Morphological Dilation | |

plot.bermantest | Plot Result of Berman Test | |

marks.tess | Marks of a Tessellation | |

pairdist.psp | Pairwise distances between line segments | |

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

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

bei | Tropical rain forest trees | |

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

AreaInter | The Area Interaction Point Process Model | |

parameters | Extract Model Parameters in Understandable Form | |

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

lohboot | Bootstrap Confidence Bands for Summary Function | |

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

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

is.rectangle | Determine Type of Window | |

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

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

dirichletVertices | Vertices and Edges of Dirichlet Tessellation | |

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

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

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

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

nnwhich.ppx | Nearest Neighbours in Any Dimensions | |

colourtools | Convert and Compare Colours in Different Formats | |

lengths.psp | Lengths of Line Segments | |

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

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

midpoints.psp | Midpoints of Line Segment Pattern | |

periodify | Make Periodic Copies of a Spatial Pattern | |

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

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

nnorient | Nearest Neighbour Orientation Distribution | |

markvario | Mark Variogram | |

compareFit | Residual Diagnostics for Multiple Fitted Models | |

pixelcentres | Extract Pixel Centres as Point Pattern | |

pixellate | Convert Spatial Object to Pixel Image | |

diameter.owin | Diameter of a Window | |

distmap.owin | Distance Map of Window | |

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

pairdist.ppx | Pairwise Distances in Any Dimensions | |

Gcom | Model Compensator of Nearest Neighbour Function | |

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

plot.listof | Plot a List of Things | |

affine.owin | Apply Affine Transformation To Window | |

persp.im | Perspective Plot of Pixel Image | |

markconnect | Mark Connection Function | |

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

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

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

pairdist.pp3 | Pairwise distances in Three Dimensions | |

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

owin.object | Class owin | |

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

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

urkiola | Urkiola Woods Point Pattern | |

marks | Marks of a Point Pattern | |

methods.units | Methods for Units | |

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

lut | Lookup Tables | |

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

methods.fii | Methods for Fitted Interactions | |

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

angles.psp | Orientation Angles of Line Segments | |

integral.msr | Integral of a Measure | |

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

perimeter | Perimeter Length of Window | |

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

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

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

Jmulti | Marked J Function | |

hybrid.family | Hybrid Interaction Family | |

triplet.family | Triplet Interaction Family | |

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

affine | Apply Affine Transformation | |

heather | Diggle's Heather Data | |

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

Gres | Residual G Function | |

im | Create a Pixel Image Object | |

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

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

pcfmulti | Marked pair correlation function | |

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

Jest | Estimate the J-function | |

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

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

intersect.tess | Intersection of Two Tessellations | |

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

trim.rectangle | Cut margins from rectangle | |

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

plot.envelope | Plot a Simulation Envelope | |

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

Softcore | The Soft Core Point Process Model | |

addvar | Added Variable Plot for Point Process Model | |

Tstat | Third order summary statistic | |

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

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

as.owin | Convert Data To Class owin | |

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

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

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

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

harmonic | Basis for Harmonic Functions | |

fryplot | Fry Plot of Point Pattern | |

ellipse | Elliptical Window. | |

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

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

plot.cdftest | Plot a Spatial Distribution Test | |

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

Geyer | Geyer's Saturation Point Process Model | |

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

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

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## Details

Nickname | Gift Horse |

Date | 2015-12-29 |

License | GPL (>= 2) |

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

LazyData | true |

NeedsCompilation | yes |

ByteCompile | true |

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

Packaged | 2015-12-29 09:03:39 UTC; adrian |

Repository | CRAN |

Date/Publication | 2015-12-29 22:08:27 |

imports | abind , deldir (>= 0.0-21) , goftest , Matrix , mgcv , polyclip (>= 1.3-0) , tensor |

depends | base (>= 3.2.2) , graphics , grDevices , methods , nlme , R (>= 3.2.2) , stats , utils |

suggests | gsl , locfit , maptools , RandomFields (>= 3.0.0) , rpanel , sm , spatial , tkrplot |

Contributors | Adrian Baddeley, Rolf Turner, Ege Rubak |

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