spatstat v1.36-0
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Spatial Point Pattern analysis, model-fitting, simulation, tests
A package for analysing spatial data, mainly Spatial Point Patterns, including multitype/marked points and spatial covariates, in any two-dimensional spatial region. Also supports three-dimensional point patterns, and space-time point patterns in any number of dimensions.
Contains over 1500 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 and tessellations.
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 similar to glm. Types of models include Poisson, Gibbs, Cox and cluster point processes. Models may involve dependence on covariates, interpoint interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods.
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 | |
| Gcom | Model Compensator of Nearest Neighbour Function | |
| Gdot | Multitype Nearest Neighbour Distance Function (i-to-any) | |
| Extract.quad | Subset of Quadrature Scheme | |
| Extract.msr | Extract Subset of Signed or Vector Measure | |
| G3est | Nearest Neighbour Distance Distribution Function of a Three-Dimensional Point Pattern | |
| Lcross.inhom | Inhomogeneous Cross Type L Function | |
| Gfox | Foxall's Distance Functions | |
| Geyer | Geyer's Saturation Point Process Model | |
| affine.psp | Apply Affine Transformation To Line Segment Pattern | |
| LennardJones | The Lennard-Jones Potential | |
| Emark | Diagnostics for random marking | |
| Gcross | Multitype Nearest Neighbour Distance Function (i-to-j) | |
| as.rectangle | Window Frame | |
| DiggleGratton | Diggle-Gratton model | |
| MultiStrauss | The Multitype Strauss Point Process Model | |
| Hardcore | The Hard Core Point Process Model | |
| Extract.listof | Extract or Replace Subset of a List of Things | |
| Iest | Estimate the I-function | |
| Jcross | Multitype J Function (i-to-j) | |
| Gmulti | Marked Nearest Neighbour Distance Function | |
| BadGey | Hybrid Geyer Point Process Model | |
| Smooth.ppp | Spatial smoothing of observations at irregular points | |
| affine.lpp | Apply Geometrical Transformations to Point Pattern on a Linear Network | |
| Ord | Generic Ord Interaction model | |
| Extract.ppp | Extract or Replace Subset of Point Pattern | |
| Ldot | Multitype L-function (i-to-any) | |
| Jest | Estimate the J-function | |
| Smooth | Spatial smoothing of data | |
| Linhom | L-function | |
| Kest | K-function | |
| Extract.fasp | Extract Subset of Function Array | |
| coef.mppm | Coefficients of Point Process Model Fitted to Multiple Point Patterns | |
| bounding.box.xy | Convex Hull of Points | |
| MultiHard | The Multitype Hard Core Point Process Model | |
| AreaInter | The Area Interaction Point Process Model | |
| as.data.frame.hyperframe | Coerce Hyperframe to Data Frame | |
| Kdot.inhom | Inhomogeneous Multitype K Dot Function | |
| as.data.frame.psp | Coerce Line Segment Pattern to a Data Frame | |
| Jdot | Multitype J Function (i-to-any) | |
| F3est | Empty Space Function of a Three-Dimensional Point Pattern | |
| amacrine | Hughes' Amacrine Cell Data | |
| Kmodel | K Function or Pair Correlation Function of a Point Process Model | |
| anova.ppm | ANOVA for Fitted Point Process Models | |
| Extract.layered | Extract Subset of a Layered Object | |
| Saturated | Saturated Pairwise Interaction model | |
| Kmodel.ppm | K Function or Pair Correlation Function of Gibbs Point Process model | |
| contour.listof | Plot a List of Things | |
| Kmulti.inhom | Inhomogeneous Marked K-Function | |
| as.owin | Convert Data To Class owin | |
| Jinhom | Inhomogeneous J-function | |
| as.function.fv | Convert Function Value Table to Function | |
| OrdThresh | Ord's Interaction model | |
| Concom | The Connected Component Process Model | |
| LambertW | Lambert's W Function | |
| crossdist.lpp | Pairwise distances between two point patterns on a linear network | |
| Kcross | Multitype K Function (Cross-type) | |
| alltypes | Calculate Summary Statistic for All Types in a Multitype Point Pattern | |
| Ginhom | Inhomogeneous Nearest Neighbour Function | |
| as.interact | Extract Interaction Structure | |
| as.lpp | Convert Data to a Point Pattern on a Linear Network | |
| as.polygonal | Convert a Window to a Polygonal Window | |
| Kmodel.kppm | K Function or Pair Correlation Function of Cluster Model or Cox model | |
| Strauss | The Strauss Point Process Model | |
| Fiksel | The Fiksel Interaction | |
| bei | Tropical rain forest trees | |
| distcdf | Distribution Function of Interpoint Distance | |
| Extract.im | Extract Subset of Image | |
| bw.relrisk | Cross Validated Bandwidth Selection for Relative Risk Estimation | |
| clickpoly | Interactively Define a Polygon | |
| Pairwise | Generic Pairwise Interaction model | |
| Kinhom | Inhomogeneous K-function | |
| Extract.splitppp | Extract or Replace Sub-Patterns | |
| corners | Corners of a rectangle | |
| bw.ppl | Likelihood Cross Validation Bandwidth Selection for Kernel Density | |
| Kmeasure | Reduced Second Moment Measure | |
| dirichlet.weights | Compute Quadrature Weights Based on Dirichlet Tessellation | |
| Kest.fft | K-function using FFT | |
| density.psp | Kernel Smoothing of Line Segment Pattern | |
| diameter.owin | Diameter of a Window | |
| Extract.owin | Extract Subset of Window | |
| adaptive.density | Intensity Estimate of Point Pattern Using Tessellation | |
| Poisson | Poisson Point Process Model | |
| Extract.lpp | Extract Subset of Point Pattern on Linear Network | |
| colourtools | Convert and Compare Colours in Different Formats | |
| clmfires | Castilla-La Mancha Forest Fires | |
| distfun.lpp | Distance Map on Linear Network | |
| allstats | Calculate four standard summary functions of a point pattern. | |
| anova.mppm | ANOVA for Fitted Multiple Point Process Models | |
| append.psp | Combine Two Line Segment Patterns | |
| Lcross | Multitype L-function (cross-type) | |
| data.ppm | Extract Original Data from a Fitted Point Process Model | |
| dfbetas.ppm | Parameter influence measure | |
| centroid.owin | Centroid of a window | |
| Hybrid | Hybrid Interaction Point Process Model | |
| as.mask | Pixel Image Approximation of a Window | |
| Smooth.msr | Smooth a Signed or Vector-Valued Measure | |
| Hest | Spherical Contact Distribution Function | |
| as.hyperframe.ppx | Extract coordinates and marks of multidimensional point pattern | |
| ants | Harkness-Isham ants' nests data | |
| addvar | Added Variable Plot for Point Process Model | |
| cbind.hyperframe | Combine Hyperframes by Rows or by Columns | |
| clickjoin | Interactively join vertices on a plot | |
| bronzefilter | Bronze gradient filter data | |
| by.ppp | Apply a Function to a Point Pattern Broken Down by Factor | |
| diagnose.ppm | Diagnostic Plots for Fitted Point Process Model | |
| compatible | Test Whether Objects Are Compatible | |
| clarkevans | Clark and Evans Aggregation Index | |
| Kscaled | Locally Scaled K-function | |
| bw.smoothppp | Cross Validated Bandwidth Selection for Spatial Smoothing | |
| affine.ppp | Apply Affine Transformation To Point Pattern | |
| convolve.im | Convolution of Pixel Images | |
| as.psp | Convert Data To Class psp | |
| as.tess | Convert Data To Tessellation | |
| chorley | Chorley-Ribble Cancer Data | |
| as.matrix.owin | Convert Pixel Image to Matrix | |
| by.im | Apply Function to Image Broken Down by Factor | |
| areaGain | Difference of Disc Areas | |
| affine.im | Apply Affine Transformation To Pixel Image | |
| bw.frac | Bandwidth Selection Based on Window Geometry | |
| integral.msr | Integral of a Measure | |
| Softcore | The Soft Core Point Process Model | |
| dirichlet | Dirichlet Tessellation of Point Pattern | |
| delaunay | Delaunay Triangulation of Point Pattern | |
| envelope.lpp | Envelope for Point Patterns on Linear Network | |
| Triplets | The Triplet Point Process Model | |
| dclf.test | Diggle-Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests | |
| identify.ppp | Identify Points in a Point Pattern | |
| distmap | Distance Map | |
| formula.fv | Extract or Change the Plot Formula for a Function Value Table | |
| MultiStraussHard | The Multitype/Hard Core Strauss Point Process Model | |
| fryplot | Fry Plot of Point Pattern | |
| chop.tess | Subdivide a Window or Tessellation using a Set of Lines | |
| is.lpp | Test Whether An Object Is A Point Pattern on a Linear Network | |
| demohyper | Demonstration Example of Hyperframe of Spatial Data | |
| is.marked.ppm | Test Whether A Point Process Model is Marked | |
| blur | Apply Gaussian Blur to a Pixel Image | |
| flipxy | Exchange X and Y Coordinates | |
| markcorr | Mark Correlation Function | |
| envelope.envelope | Recompute Envelopes | |
| bermantest | Berman's Tests for Point Process Model | |
| crossdist.default | Pairwise distances between two different sets of points | |
| bdist.pixels | Distance to Boundary of Window | |
| layered | Create List of Plotting Layers | |
| bdist.points | Distance to Boundary of Window | |
| eval.im | Evaluate Expression Involving Pixel Images | |
| envelope.pp3 | Simulation Envelopes of Summary Function for 3D Point Pattern | |
| diameter | Diameter of an Object | |
| heather | Diggle's Heather Data | |
| bounding.box | Bounding Box of a Window or Point Pattern | |
| default.expand | Default Expansion Rule for Simulation of Model | |
| crossdist.ppx | Pairwise Distances Between Two Different Multi-Dimensional Point Patterns | |
| as.mask.psp | Convert Line Segment Pattern to Binary Pixel Mask | |
| area.owin | Area of a Window | |
| default.rmhcontrol | Set Default Control Parameters for Metropolis-Hastings Algorithm. | |
| fasp.object | Function Arrays for Spatial Patterns | |
| is.convex | Test Whether a Window is Convex | |
| coords | Extract or Change Coordinates of a Spatial or Spatiotemporal Point Pattern | |
| anova.lppm | ANOVA for Fitted Point Process Models on Linear Network | |
| is.ppp | Test Whether An Object Is A Point Pattern | |
| discretise | Safely Convert Point Pattern Window to Binary Mask | |
| anemones | Beadlet Anemones Data | |
| fitin.ppm | Extract the Interaction from a Fitted Point Process Model | |
| pixellate | Convert Spatial Object to Pixel Image | |
| complement.owin | Take Complement of a Window | |
| inforder.family | Infinite Order Interaction Family | |
| disc | Circular Window | |
| is.hybrid | Test Whether Object is a Hybrid | |
| predict.lppm | Predict Point Process Model on Linear Network | |
| anova.slrm | Analysis of Deviance for Spatial Logistic Regression Models | |
| border | Border Region of a Window | |
| as.ppp | Convert Data To Class ppp | |
| betacells | Beta Ganglion Cells in Cat Retina | |
| expand.owin | Apply Expansion Rule | |
| bw.scott | Scott's Rule for Bandwidth Selection for Kernel Density | |
| bramblecanes | Hutchings' Bramble Canes data | |
| harmonic | Basis for Harmonic Functions | |
| hamster | Aherne's hamster tumour data | |
| bw.diggle | Cross Validated Bandwidth Selection for Kernel Density | |
| distmap.psp | Distance Map of Line Segment Pattern | |
| connected | Connected components | |
| clickppp | Interactively Add Points | |
| inside.owin | Test Whether Points Are Inside A Window | |
| is.rectangle | Determine Type of Window | |
| japanesepines | Japanese Pines Point Pattern | |
| lgcp.estpcf | Fit a Log-Gaussian Cox Point Process by Minimum Contrast | |
| distmap.owin | Distance Map of Window | |
| dilation | Morphological Dilation | |
| exactMPLEstrauss | Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process | |
| is.ppm | Test Whether An Object Is A Fitted Point Process Model | |
| erosion | Morphological Erosion | |
| linim | Create Pixel Image on Linear Network | |
| eval.fv | Evaluate Expression Involving Functions | |
| is.multitype | Test whether Object is Multitype | |
| closepairs | Close Pairs of Points | |
| crossdist | Pairwise distances | |
| kaplan.meier | Kaplan-Meier Estimator using Histogram Data | |
| clusterset | Allard-Fraley Estimator of Cluster Feature | |
| as.matrix.im | Convert Pixel Image to Matrix or Array | |
| is.multitype.ppm | Test Whether A Point Process Model is Multitype | |
| Kcom | Model Compensator of K Function | |
| applynbd | Apply Function to Every Neighbourhood in a Point Pattern | |
| finpines | Pine saplings in Finland. | |
| crossdist.ppp | Pairwise distances between two different point patterns | |
| integral.im | Integral of a Pixel Image | |
| density.ppp | Kernel Smoothed Intensity of Point Pattern | |
| cut.im | Convert Pixel Image from Numeric to Factor | |
| StraussHard | The Strauss / Hard Core Point Process Model | |
| funxy | Spatial Function Class | |
| im.object | Class of Images | |
| compatible.fasp | Test Whether Function Arrays Are Compatible | |
| chicago | Chicago Street Crime Data | |
| delaunay.distance | Distance on Delaunay Triangulation | |
| model.matrix.slrm | Extract Design Matrix from Spatial Logistic Regression Model | |
| duplicated.ppp | Determine Duplicated Points in a Spatial Point Pattern | |
| linearKdot.inhom | Inhomogeneous multitype K Function (Dot-type) for Linear Point Pattern | |
| as.data.frame.im | Convert Pixel Image to Data Frame | |
| methods.fii | Methods for Fitted Interactions | |
| hextess | Hexagonal Grid or Tessellation | |
| is.multitype.ppp | Test Whether A Point Pattern is Multitype | |
| gorillas | Gorilla Nesting Sites | |
| methods.kppm | Methods for Cluster Point Process Models | |
| Kres | Residual K Function | |
| ord.family | Ord Interaction Process Family | |
| ppx | Multidimensional Space-Time Point Pattern | |
| lineardisc | Compute Disc of Given Radius in Linear Network | |
| is.empty | Test Whether An Object Is Empty | |
| model.frame.ppm | Extract the Variables in a Point Process Model | |
| methods.funxy | Methods for Spatial Functions | |
| infline | Infinite Straight Lines | |
| lohboot | Bootstrap Confidence Bands for Summary Function | |
| dilated.areas | Areas of Morphological Dilations | |
| endpoints.psp | Endpoints of Line Segment Pattern | |
| pairsat.family | Saturated Pairwise Interaction Point Process Family | |
| cauchy.estpcf | Fit the Neyman-Scott cluster process with Cauchy kernel | |
| ganglia | Beta Ganglion Cells in Cat Retina, Old Version | |
| fitted.ppm | Fitted Conditional Intensity for Point Process Model | |
| coef.slrm | Coefficients of Fitted Spatial Logistic Regression Model | |
| distmap.ppp | Distance Map of Point Pattern | |
| fv | Create a Function Value Table | |
| as.hyperframe | Convert Data to Hyperframe | |
| levelset | Level Set of a Pixel Image | |
| edge.Trans | Translation Edge Correction | |
| box3 | Three-Dimensional Box | |
| plot.envelope | Plot a Simulation Envelope | |
| logLik.ppm | Log Likelihood and AIC for Point Process Model | |
| methods.pp3 | Methods for three-dimensional point patterns | |
| methods.linfun | Methods for Functions on Linear Network | |
| nnwhich.ppx | Nearest Neighbours in Any Dimensions | |
| nsegments | Number of Line Segments in a Line Segment Pattern | |
| affine | Apply Affine Transformation | |
| pixellate.ppp | Convert Point Pattern to Pixel Image | |
| methods.rhohat | Methods for Intensity Functions of Spatial Covariate | |
| Extract.tess | Extract or Replace Subset of Tessellation | |
| kppm | Fit Cluster or Cox Point Process Model | |
| intensity.ppm | Intensity of Fitted Point Process Model | |
| edges2triangles | List Triangles in a Graph | |
| circumradius | Circumradius and Diameter of a Linear Network | |
| effectfun | Compute Fitted Effect of a Spatial Covariate in a Point Process Model | |
| nztrees | New Zealand Trees Point Pattern | |
| logLik.mppm | Log Likelihood for Poisson Point Process Model | |
| flu | Influenza Virus Proteins | |
| is.im | Test Whether An Object Is A Pixel Image | |
| linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |
| linearmarkequal | Mark Connection Function for Multitype Point Pattern on Linear Network | |
| pcf.fasp | Pair Correlation Function obtained from array of K functions | |
| im.apply | Apply Function Pixelwise to List of Images | |
| Extract.linnet | Extract Subset of Linear Network | |
| as.box3 | Convert Data to Three-Dimensional Box | |
| crossing.psp | Crossing Points of Two Line Segment Patterns | |
| compatible.im | Test Whether Pixel Images Are Compatible | |
| gridweights | Compute Quadrature Weights Based on Grid Counts | |
| matclust.estK | Fit the Matern Cluster Point Process by Minimum Contrast | |
| idw | Inverse-distance weighted smoothing of observations at irregular points | |
| interp.colourmap | Interpolate smoothly between specified colours | |
| pcf.ppp | Pair Correlation Function of Point Pattern | |
| is.marked | Test Whether Marks Are Present | |
| linearKcross | Multitype K Function (Cross-type) for Linear Point Pattern | |
| linearpcf | Linear Pair Correlation Function | |
| colourmap | Colour Lookup Tables | |
| fv.object | Function Value Table | |
| plot.listof | Plot a List of Things | |
| concatxy | Concatenate x,y Coordinate Vectors | |
| objsurf | Objective Function Surface | |
| istat | Point and Click Interface for Exploratory Analysis of Point Pattern | |
| methods.rho2hat | Methods for Intensity Functions of Two Spatial Covariates | |
| gridcentres | Rectangular grid of points | |
| linearpcfdot | Multitype Pair Correlation Function (Dot-type) for Linear Point Pattern | |
| hybrid.family | Hybrid Interaction Family | |
| nearestsegment | Find Line Segment Nearest to Each Point | |
| pcfinhom | Inhomogeneous Pair Correlation Function | |
| paracou | Kimboto trees at Paracou, French Guiana | |
| letterR | Window in Shape of Letter R | |
| distfun | Distance Map as a Function | |
| linearpcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-type) for Linear Point Pattern | |
| hyytiala | Scots pines and other trees at Hyytiala | |
| is.stationary | Recognise Stationary and Poisson Point Process Models | |
| nbfires | Point Patterns of New Brunswick Forest Fires | |
| linfun | Function on a Linear Network | |
| intersect.owin | Intersection, Union or Set Subtraction of Two Windows | |
| Extract.psp | Extract Subset of Line Segment Pattern | |
| latest.news | Print News About Latest Version of Package | |
| dclf.progress | Progress Plot of Test of Spatial Pattern | |
| fitted.mppm | Fitted Conditional Intensity for Multiple Point Process Model | |
| Tstat | Third order summary statistic | |
| eroded.areas | Areas of Morphological Erosions | |
| fvnames | Abbreviations for Groups of Columns in Function Value Table | |
| crossdist.pp3 | Pairwise distances between two different three-dimensional point patterns | |
| linearKdot | Multitype K Function (Dot-type) for Linear Point Pattern | |
| predict.kppm | Prediction from a Fitted Cluster Point Process Model | |
| methods.lpp | Methods for Point Patterns on a Linear Network | |
| incircle | Find Largest Circle Inside Window | |
| im | Create a Pixel Image Object | |
| lurking | Lurking variable plot | |
| pairdist.psp | Pairwise distances between line segments | |
| identify.psp | Identify Segments in a Line Segment Pattern | |
| nncross | Nearest Neighbours Between Two Patterns | |
| predict.mppm | Prediction for Fitted Multiple Point Process Model | |
| rmhstart | Determine Initial State for Metropolis-Hastings Simulation. | |
| nndist.lpp | Nearest neighbour distances on a linear network | |
| npoints | Number of Points in a Point Pattern | |
| intensity.quadratcount | Intensity Estimates Using Quadrat Counts | |
| quadrat.test | Dispersion Test for Spatial Point Pattern Based on Quadrat Counts | |
| rGaussPoisson | Simulate Gauss-Poisson Process | |
| linearpcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Dot-type) for Linear Point Pattern | |
| parres | Partial Residuals for Point Process Model | |
| npfun | Dummy Function Returns Number of Points | |
| dummy.ppm | Extract Dummy Points Used to Fit a Point Process Model | |
| reach | Interaction Distance of a Point Process | |
| localpcf | Local pair correlation function | |
| boxx | Multi-Dimensional Box | |
| gordon | People in Gordon Square | |
| rHardcore | Perfect Simulation of the Hardcore Process | |
| print.ppm | Print a Fitted Point Process Model | |
| clip.infline | Intersect Infinite Straight Lines with a Window | |
| plot.pp3 | Plot a three-dimensional point pattern | |
| pixelquad | Quadrature Scheme Based on Pixel Grid | |
| midpoints.psp | Midpoints of Line Segment Pattern | |
| areaLoss | Difference of Disc Areas | |
| plot.scan.test | Plot Result of Scan Test | |
| pool.envelope | Pool Data from Several Envelopes | |
| closing | Morphological Closing | |
| compareFit | Residual Diagnostics for Multiple Fitted Models | |
| lpp | Create Point Pattern on Linear Network | |
| methods.objsurf | Methods for Objective Function Surfaces | |
| leverage.ppm | Leverage Measure for Spatial Point Process Model | |
| pcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Type-i-To-Any-Type) | |
| rescale | Convert dataset to another unit of length | |
| methods.layered | Methods for Layered Objects | |
| eem | Exponential Energy Marks | |
| plot.bermantest | Plot Result of Berman Test | |
| pixellate.owin | Convert Window to Pixel Image | |
| as.im | Convert to Pixel Image | |
| pairs.im | Scatterplot Matrix for Pixel Images | |
| model.depends | Identify Covariates Involved in each Model Term | |
| methods.units | Methods for Units | |
| markconnect | Mark Connection Function | |
| marktable | Tabulate Marks in Neighbourhood of Every Point in a Point Pattern | |
| pcfmulti | Marked pair correlation function | |
| plot.leverage.ppm | Plot Leverage Function | |
| formula.ppm | Model Formulae for Gibbs Point Process Models | |
| nnmark | Mark of Nearest Neighbour | |
| plot.quadrattest | Display the result of a quadrat counting test. | |
| layout.boxes | Generate a Row or Column Arrangement of Rectangles. | |
| bw.stoyan | Stoyan's Rule of Thumb for Bandwidth Selection | |
| edges2vees | List Dihedral Triples in a Graph | |
| imcov | Spatial Covariance of a Pixel Image | |
| intensity | Intensity of a Dataset or a Model | |
| ppp | Create a Point Pattern | |
| kstest.ppm | Kolmogorov-Smirnov Test for Point Pattern or Point Process Model | |
| plot.im | Plot a Pixel Image | |
| murchison | Murchison gold deposits | |
| lansing | Lansing Woods Point Pattern | |
| is.owin | Test Whether An Object Is A Window | |
| predict.ppm | Prediction from a Fitted Point Process Model | |
| owin | Create a Window | |
| project.ppm | Force Point Process Model to be Valid | |
| nncross.pp3 | Nearest Neighbours Between Two Patterns in 3D | |
| persp.im | Perspective Plot of Pixel Image | |
| miplot | Morishita Index Plot | |
| pairdist.default | Pairwise distances | |
| nndist.ppx | Nearest Neighbour Distances in Any Dimensions | |
| quadrats | Divide Region into Quadrats | |
| edge.Ripley | Ripley's Isotropic Edge Correction | |
| pppdist | Distance Between Two Point Patterns | |
| markvario | Mark Variogram | |
| discpartarea | Area of Part of Disc | |
| qqplot.ppm | Q-Q Plot of Residuals from Fitted Point Process Model | |
| pcfdot | Multitype pair correlation function (i-to-any) | |
| localK | Neighbourhood density function | |
| rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |
| osteo | Osteocyte Lacunae Data: Replicated Three-Dimensional Point Patterns | |
| quadscheme | Generate a Quadrature Scheme from a Point Pattern | |
| influence.ppm | Influence Measure for Spatial Point Process Model | |
| residualspaper | Data and Code From JRSS Discussion Paper on Residuals | |
| demopat | Artificial Data Point Pattern | |
| spokes | Spokes pattern of dummy points | |
| plot.plotppm | Plot a plotppm Object Created by plot.ppm | |
| mincontrast | Method of Minimum Contrast | |
| linearK | Linear K Function | |
| shift.ppp | Apply Vector Translation To Point Pattern | |
| stieltjes | Compute Integral of Function Against Cumulative Distribution | |
| marks.psp | Marks of a Line Segment Pattern | |
| print.psp | Print Brief Details of a Line Segment Pattern Dataset | |
| rotate.owin | Rotate a Window | |
| intersect.tess | Intersection of Two Tessellations | |
| quad.object | Class of Quadrature Schemes | |
| ppm.object | Class of Fitted Point Process Models | |
| matchingdist | Distance for a Point Pattern Matching | |
| fitted.slrm | Fitted Probabilities for Spatial Logistic Regression | |
| rshift.splitppp | Randomly Shift a List of Point Patterns | |
| redwoodfull | California Redwoods Point Pattern (Entire Dataset) | |
| rpoislpp | Poisson Point Process on a Linear Network | |
| longleaf | Longleaf Pines Point Pattern | |
| kstest.mppm | Kolmogorov-Smirnov Test for Multiple Point Process Model | |
| plot.colourmap | Plot a Colour Map | |
| rVarGamma | Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel | |
| pairdist | Pairwise distances | |
| sharpen | Data Sharpening of Point Pattern | |
| spatstat-internal | Internal spatstat functions | |
| methods.ppx | Methods for Multidimensional Space-Time Point Patterns | |
| trim.rectangle | Cut margins from rectangle | |
| tiles | Extract List of Tiles in a Tessellation | |
| copper | Berman-Huntington points and lines data | |
| localKinhom | Inhomogeneous Neighbourhood Density Function | |
| lgcp.estK | Fit a Log-Gaussian Cox Point Process by Minimum Contrast | |
| summary.quad | Summarizing a Quadrature Scheme | |
| linnet | Create a Linear Network | |
| bdist.tiles | Distance to Boundary of Window | |
| pairwise.family | Pairwise Interaction Process Family | |
| methods.box3 | Methods for Three-Dimensional Box | |
| rescale.ppp | Convert Point Pattern to Another Unit of Length | |
| rshift.ppp | Randomly Shift a Point Pattern | |
| linearKcross.inhom | Inhomogeneous multitype K Function (Cross-type) for Linear Point Pattern | |
| methods.slrm | Methods for Spatial Logistic Regression Models | |
| deriv.fv | Calculate Derivative of Function Values | |
| affine.owin | Apply Affine Transformation To Window | |
| rlinegrid | Generate grid of parallel lines with random displacement | |
| reload.or.compute | Compute Unless Previously Saved | |
| quadrat.test.mppm | Chi-Squared Test for Multiple Point Process Model Based on Quadrat Counts | |
| nndensity.ppp | Estimate Intensity of Point Pattern Using Nearest Neighbour Distances | |
| sidelengths.owin | Side Lengths of Enclosing Rectangle of a Window | |
| multiplicity.ppp | Count Multiplicity of Duplicate Points | |
| simulate.kppm | Simulate a Fitted Cluster Point Process Model | |
| nndist.psp | Nearest neighbour distances between line segments | |
| overlap.owin | Compute Area of Overlap | |
| pppmatching | Create a Point Matching | |
| nndist.pp3 | Nearest neighbour distances in three dimensions | |
| rmh.default | Simulate Point Process Models using the Metropolis-Hastings Algorithm. | |
| rcell | Simulate Baddeley-Silverman Cell Process | |
| pool | Pool Data | |
| opening | Morphological Opening | |
| quantile.ewcdf | Quantiles of Weighted Empirical Cumulative Distribution Function | |
| rescale.psp | Convert Line Segment Pattern to Another Unit of Length | |
| stratrand | Stratified random point pattern | |
| nnmap | K-th Nearest Point Map | |
| rpoisline | Generate Poisson Random Line Process | |
| pcf3est | Pair Correlation Function of a Three-Dimensional Point Pattern | |
| rescale.owin | Convert Window to Another Unit of Length | |
| plot.kstest | Plot a Spatial Kolmogorov-Smirnov Test | |
| tweak.colourmap | Change Colour Values in a Colour Map | |
| nnfun | Nearest Neighbour Index Map as a Function | |
| markstat | Summarise Marks in Every Neighbourhood in a Point Pattern | |
| simplify.owin | Approximate a Polygon by a Simpler Polygon | |
| mean.im | Maximum, Minimum, Mean, Median, Range or Sum of Pixel Values in an Image | |
| quadrat.test.splitppp | Dispersion Test of CSR for Split Point Pattern Based on Quadrat Counts | |
| plot.ppm | plot a Fitted Point Process Model | |
| tile.areas | Compute Areas of Tiles in a Tessellation | |
| connected.ppp | Connected components of a point pattern | |
| plot.psp | plot a Spatial Line Segment Pattern | |
| rCauchy | Simulate Neyman-Scott Point Process with Cauchy cluster kernel | |
| rmhmodel.default | Build Point Process Model for Metropolis-Hastings Simulation. | |
| summary.listof | Summary of a List of Things | |
| matclust.estpcf | Fit the Matern Cluster Point Process by Minimum Contrast Using Pair Correlation | |
| psstA | Pseudoscore Diagnostic For Fitted Model against Area-Interaction Alternative | |
| square | Square Window | |
| progressreport | Print Progress Reports | |
| superimpose | Superimpose Several Geometric Patterns | |
| summary.im | Summarizing a Pixel Image | |
| shift.owin | Apply Vector Translation To Window | |
| convexhull | Convex Hull | |
| residuals.mppm | Residuals for Point Process Model Fitted to Multiple Point Patterns | |
| simulate.slrm | Simulate a Fitted Spatial Logistic Regression Model | |
| summary.splitppp | Summary of a Split Point Pattern | |
| rotate.im | Rotate a Pixel Image | |
| summary.owin | Summary of a Spatial Window | |
| rlabel | Random Re-Labelling of Point Pattern | |
| rshift.psp | Randomly Shift a Line Segment Pattern | |
| rmhmodel | Define Point Process Model for Metropolis-Hastings Simulation. | |
| vcov.kppm | Variance-Covariance Matrix for a Fitted Cluster Point Process Model | |
| redwood | California Redwoods Point Pattern (Ripley's Subset) | |
| as.linim | Convert to Pixel Image on Linear Network | |
| nncross.lpp | Nearest Neighbours on a Linear Network | |
| rMosaicField | Mosaic Random Field | |
| rmh | Simulate point patterns using the Metropolis-Hastings algorithm. | |
| varblock | Estimate Variance of Summary Statistic by Subdivision | |
| pairdist.pp3 | Pairwise distances in Three Dimensions | |
| pppmatching.object | Class of Point Matchings | |
| plot.influence.ppm | Plot Influence Measure | |
| rshift | Random Shift | |
| rMatClust | Simulate Matern Cluster Process | |
| rmhexpand | Specify Simulation Window or Expansion Rule | |
| scanLRTS | Likelihood Ratio Test Statistic for Scan Test | |
| plot.linnet | Plot a linear network | |
| rMosaicSet | Mosaic Random Set | |
| pairdist.ppx | Pairwise Distances in Any Dimensions | |
| rMaternII | Simulate Matern Model II | |
| update.ppm | Update a Fitted Point Process Model | |
| shift.im | Apply Vector Translation To Pixel Image | |
| predict.slrm | Predicted or Fitted Values from Spatial Logistic Regression | |
| waka | Trees in Waka national park | |
| ponderosa | Ponderosa Pine Tree Point Pattern | |
| rThomas | Simulate Thomas Process | |
| commonGrid | Determine A Common Spatial Domain And Pixel Resolution | |
| profilepl | Profile Maximum Pseudolikelihood | |
| round.ppp | Apply Numerical Rounding to Spatial Coordinates | |
| rotate | Rotate | |
| lut | Lookup Tables | |
| runifdisc | Generate N Uniform Random Points in a Disc | |
| union.quad | Union of Data and Dummy Points | |
| plot.quad | Plot a Spatial Quadrature Scheme | |
| simdat | Simulated Point Pattern | |
| rStrauss | Perfect Simulation of the Strauss Process | |
| pool.rat | Pool Data from Several Ratio Objects | |
| rat | Ratio object | |
| scanpp | Read Point Pattern From Data File | |
| sessionLibs | Print Names and Version Numbers of Libraries Loaded | |
| pixellate.psp | Convert Line Segment Pattern to Pixel Image | |
| summary.ppm | Summarizing a Fitted Point Process Model | |
| unmark | Remove Marks | |
| quad.ppm | Extract Quadrature Scheme Used to Fit a Point Process Model | |
| quadratresample | Resample a Point Pattern by Resampling Quadrats | |
| pool.fasp | Pool Data from Several Function Arrays | |
| psst | Pseudoscore Diagnostic For Fitted Model against General Alternative | |
| print.quad | Print a Quadrature Scheme | |
| rhohat | Smoothing Estimate of Covariate Transformation | |
| psp | Create a Line Segment Pattern | |
| rgbim | Create Colour-Valued Pixel Image | |
| update.rmhcontrol | Update Control Parameters of Metropolis-Hastings Algorithm | |
| rounding | Detect Numerical Rounding | |
| simplenet | Simple Example of Linear Network | |
| quadratcount | Quadrat counting for a point pattern | |
| urkiola | Urkiola Woods Point Pattern | |
| plot.fasp | Plot a Function Array | |
| simplepanel | Simple Point-and-Click Interface Panels | |
| mppm | Fit Point Process Model to Several Point Patterns | |
| msr | Signed or Vector-Valued Measure | |
| will.expand | Test Expansion Rule | |
| plot.ppp | plot a Spatial Point Pattern | |
| slrm | Spatial Logistic Regression | |
| timed | Record the Computation Time | |
| runiflpp | Uniform Random Points on a Linear Network | |
| rknn | Theoretical Distribution of Nearest Neighbour Distance | |
| sumouter | Compute Quadratic Forms | |
| cells | Biological Cells Point Pattern | |
| rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |
| pcf | Pair Correlation Function | |
| nearest.raster.point | Find Pixel Nearest to a Given Point | |
| is.marked.ppp | Test Whether A Point Pattern is Marked | |
| rmhmodel.ppm | Interpret Fitted Model for Metropolis-Hastings Simulation. | |
| unitname | Name for Unit of Length | |
| print.ppp | Print Brief Details of a Point Pattern Dataset | |
| runifpointx | Generate N Uniform Random Points in Any Dimensions | |
| quantile.im | Sample Quantiles of Pixel Image | |
| ppp.object | Class of Point Patterns | |
| rDiggleGratton | Perfect Simulation of the Diggle-Gratton Process | |
| runifpoint3 | Generate N Uniform Random Points in Three Dimensions | |
| nnwhich.pp3 | Nearest neighbours in three dimensions | |
| rescale.im | Convert Pixel Image to Another Unit of Length | |
| reduced.sample | Reduced Sample Estimator using Histogram Data | |
| pairdist.lpp | Pairwise shortest-path distances between points on a linear network | |
| vargamma.estpcf | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel | |
| thomas.estpcf | Fit the Thomas Point Process by Minimum Contrast | |
| whist | Weighted Histogram | |
| linearpcfcross | Multitype Pair Correlation Function (Cross-type) for Linear Point Pattern | |
| rho2hat | Smoothed Relative Density of Pairs of Covariate Values | |
| print.owin | Print Brief Details of a Spatial Window | |
| with.hyperframe | Evaluate an Expression in Each Row of a Hyperframe | |
| spatstat-package | The Spatstat Package | |
| plot.msr | Plot a Signed or Vector-Valued Measure | |
| diameter.box3 | Geometrical Calculations for Three-Dimensional Box | |
| ppm | Fit Point Process Model to Data | |
| setcov | Set Covariance of a Window | |
| rstrat | Simulate Stratified Random Point Pattern | |
| rmpoint | Generate N Random Multitype Points | |
| plot.quadratcount | Plot Quadrat Counts | |
| beginner | Print Introduction For Beginners | |
| scalardilate | Apply Scalar Dilation | |
| rSSI | Simulate Simple Sequential Inhibition | |
| rLGCP | Simulate Log-Gaussian Cox Process | |
| rpoispp | Generate Poisson Point Pattern | |
| rPoissonCluster | Simulate Poisson Cluster Process | |
| plot.lppm | Plot a Fitted Point Process Model on a Linear Network | |
| psp.object | Class of Line Segment Patterns | |
| run.simplepanel | Run Point-and-Click Interface | |
| update.kppm | Update a Fitted Cluster Point Process Model | |
| model.images | Compute Images of Constructed Covariates | |
| simulate.ppm | Simulate a Fitted Gibbs Point Process Model | |
| rotate.psp | Rotate a Line Segment Pattern | |
| summary.psp | Summary of a Line Segment Pattern Dataset | |
| relrisk | Nonparametric Estimate of Spatially-Varying Relative Risk | |
| rStraussHard | Perfect Simulation of the Strauss-Hardcore Process | |
| volume | Volume of an Object | |
| print.im | Print Brief Details of an Image | |
| methods.boxx | Methods for Multi-Dimensional Box | |
| shift | Apply Vector Translation | |
| rNeymanScott | Simulate Neyman-Scott Process | |
| rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |
| K3est | K-function of a Three-Dimensional Point Pattern | |
| subfits | Extract List of Individual Point Process Models | |
| pointsOnLines | Place Points Evenly Along Specified Lines | |
| plot.splitppp | Plot a List of Point Patterns | |
| rmhmodel.list | Define Point Process Model for Metropolis-Hastings Simulation. | |
| Jmulti | Marked J Function | |
| unnormdensity | Weighted kernel smoother | |
| rcellnumber | Generate Random Numbers of Points for Cell Process | |
| vertices | Vertices of a Window | |
| waterstriders | Waterstriders data. Three independent replications of a point pattern formed by insects. | |
| runifpoint | Generate N Uniform Random Points | |
| plot.slrm | Plot a Fitted Spatial Logistic Regression | |
| swedishpines | Swedish Pines Point Pattern | |
| triplet.family | Triplet Interaction Family | |
| thomas.estK | Fit the Thomas Point Process by Minimum Contrast | |
| rpoislinetess | Poisson Line Tessellation | |
| valid.ppm | Check Whether Point Process Model is Valid | |
| vcov.mppm | Calculate Variance-Covariance Matrix for Fitted Multiple Point Process Model | |
| pairdist.ppp | Pairwise distances | |
| unique.ppp | Extract Unique Points from a Spatial Point Pattern | |
| plot.owin | Plot a Spatial Window | |
| plot.tess | Plot a tessellation | |
| rMaternI | Simulate Matern Model I | |
| pcf.fv | Pair Correlation Function obtained from K Function | |
| project2segment | Move Point To Nearest Line | |
| raster.x | Cartesian Coordinates for a Pixel Raster | |
| plot.mppm | plot a Fitted Multiple Point Process Model | |
| psstG | Pseudoscore Diagnostic For Fitted Model against Saturation Alternative | |
| rjitter | Random Perturbation of a Point Pattern | |
| tess | Create a Tessellation | |
| tilenames | Names of Tiles in a Tessellation | |
| plot.hyperframe | Plot Entries in a Hyperframe | |
| rmh.ppm | Simulate from a Fitted Point Process Model | |
| quadscheme.logi | Generate a Logistic Regression Quadrature Scheme from a Point Pattern | |
| rmpoispp | Generate Multitype Poisson Point Pattern | |
| plot.kppm | Plot a fitted cluster point process | |
| shapley | Galaxies in the Shapley Supercluster | |
| spatstat-deprecated | Deprecated spatstat functions | |
| pcfcross | Multitype pair correlation function (cross-type) | |
| spatstat.options | Internal Options in Spatstat Package | |
| rmhcontrol | Set Control Parameters for Metropolis-Hastings Algorithm. | |
| zapsmall.im | Rounding of Pixel Values | |
| rsyst | Simulate systematic random point pattern | |
| scaletointerval | Rescale Data to Lie Between Specified Limits | |
| periodify | Make Periodic Copies of a Spatial Pattern | |
| Extract.fv | Extract Subset of Function Values | |
| PairPiece | The Piecewise Constant Pairwise Interaction Point Process Model | |
| DiggleGatesStibbard | Diggle-Gates-Stibbard Point Process Model | |
| SatPiece | Piecewise Constant Saturated Pairwise Interaction Point Process Model | |
| Lest | L-function | |
| as.fv | Convert Data To Class fv | |
| Kdot | Multitype K Function (i-to-any) | |
| bind.fv | Combine Function Value Tables | |
| compatible.fv | Test Whether Function Objects Are Compatible | |
| interp.im | Interpolate a Pixel Image | |
| deltametric | Delta Metric | |
| default.dummy | Generate a Default Pattern of Dummy Points | |
| envelope | Simulation Envelopes of Summary Function | |
| ippm | Optimise Irregular Trend Parameters in Point Process Model | |
| iplot | Point and Click Interface for Displaying Spatial Data | |
| lppm | Fit Point Process Model to Point Pattern on Linear Network | |
| model.matrix.ppm | Extract Design Matrix from Point Process Model | |
| markcorrint | Mark Correlation Integral | |
| owin.object | Class owin | |
| humberside | Humberside Data on Childhood Leukaemia and Lymphoma | |
| spruces | Spruces Point Pattern | |
| split.im | Divide Image Into Sub-images | |
| shift.psp | Apply Vector Translation To Line Segment Pattern | |
| transect.im | Pixel Values Along a Transect | |
| residuals.ppm | Residuals for Fitted Point Process Model | |
| with.fv | Evaluate an Expression in a Function Table | |
| pcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-Type) | |
| pyramidal | Pyramidal Neurons in Cingulate Cortex | |
| pp3 | Three Dimensional Point Pattern | |
| Gest | Nearest Neighbour Distance Function G | |
| Kmulti | Marked K-Function | |
| beachcolours | Create Colour Scheme for a Range of Numbers | |
| as.data.frame.ppp | Coerce Point Pattern to a Data Frame | |
| cut.ppp | Classify Points in a Point Pattern | |
| clarkevans.test | Clark and Evans Test | |
| diameter.boxx | Geometrical Calculations for Multi-Dimensional Box | |
| hist.im | Histogram of Pixel Values in an Image | |
| Extract.ppx | Extract Subset of Multidimensional Point Pattern | |
| methods.linnet | Methods for Linear Networks | |
| marks | Marks of a Point Pattern | |
| logLik.slrm | Loglikelihood of Spatial Logistic Regression | |
| nnwhich.lpp | Identify Nearest Neighbours on a Linear Network | |
| nndist | Nearest neighbour distances | |
| plot.linim | Plot Pixel Image on Linear Network | |
| plot.fv | Plot Function Values | |
| rotate.ppp | Rotate a Point Pattern | |
| plot.layered | Layered Plot | |
| rthin | Random Thinning | |
| summary.ppp | Summary of a Point Pattern Dataset | |
| split.ppx | Divide Multidimensional Point Pattern into Sub-patterns | |
| rDGS | Perfect Simulation of the Diggle-Gates-Stibbard Process | |
| nncorr | Nearest-Neighbour Correlation Indices of Marked Point Pattern | |
| solutionset | Evaluate Logical Expression Involving Pixel Images and Return Region Where Expression is True | |
| vcov.ppm | Variance-Covariance Matrix for a Fitted Point Process Model | |
| Replace.im | Reset Values in Subset of Image | |
| Ldot.inhom | Inhomogeneous Multitype L Dot Function | |
| Kcross.inhom | Inhomogeneous Cross K Function | |
| Smooth.fv | Apply Smoothing to Function Values | |
| cauchy.estK | Fit the Neyman-Scott cluster process with Cauchy kernel | |
| coef.ppm | Coefficients of Fitted Point Process Model | |
| convexhull.xy | Convex Hull of Points | |
| hyperframe | Hyper Data Frame | |
| intensity.lpp | Empirical Intensity of Point Pattern on Linear Network | |
| eval.linim | Evaluate Expression Involving Pixel Images on Linear Network | |
| km.rs | Kaplan-Meier and Reduced Sample Estimator using Histograms | |
| ewcdf | Weighted Empirical Cumulative Distribution Function | |
| nnwhich | Nearest neighbour | |
| lengths.psp | Lengths of Line Segments | |
| perimeter | Perimeter Length of Window | |
| nnclean | Nearest Neighbour Clutter Removal | |
| methods.lppm | Methods for Fitted Point Process Models on a Linear Network | |
| mucosa | Cells in Gastric Mucosa | |
| linearmarkconnect | Mark Connection Function for Multitype Point Pattern on Linear Network | |
| pool.quadrattest | Pool Several Quadrat Tests | |
| simba | Simulated data from a two-group experiment with replication within each group. | |
| vcov.slrm | Variance-Covariance Matrix for a Fitted Spatial Logistic Regression | |
| suffstat | Sufficient Statistic of Point Process Model | |
| density.splitppp | Kernel Smoothed Intensity of Split Point Pattern | |
| Finhom | Inhomogeneous Empty Space Function | |
| angles.psp | Orientation Angles of Line Segments | |
| as.ppm | Extract Fitted Point Process Model | |
| affine.tess | Apply Geometrical Transformation To Tessellation | |
| crossdist.psp | Pairwise distances between two different line segment patterns | |
| Fest | Estimate the empty space function F | |
| collapse.fv | Collapse Several Function Tables into One | |
| dummify | Convert Data to Numeric Values by Constructing Dummy Variables | |
| contour.im | Contour plot of pixel image | |
| intensity.ppp | Empirical Intensity of Point Pattern | |
| eval.fasp | Evaluate Expression Involving Function Arrays | |
| harmonise.im | Make Pixel Images Compatible | |
| layerplotargs | Extract or Replace the Plot Arguments of a Layered Object | |
| is.subset.owin | Determine Whether One Window is Contained In Another | |
| nnfun.lpp | Nearest Neighbour Map on Linear Network | |
| linearKinhom | Inhomogeneous Linear K Function | |
| reflect | Reflect In Origin | |
| selfcrossing.psp | Crossing Points in a Line Segment Pattern | |
| ripras | Estimate window from points alone | |
| Gres | Residual G Function | |
| rpoint | Generate N Random Points | |
| scan.test | Spatial Scan Test | |
| affine.linnet | Apply Geometrical Transformations to a Linear Network | |
| rescue.rectangle | Convert Window Back To Rectangle | |
| split.ppp | Divide Point Pattern into Sub-patterns | |
| vargamma.estK | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel | |
| runifpointOnLines | Generate N Uniform Random Points On Line Segments | |
| No Results! | ||
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Details
| Nickname | Intense Scrutiny |
| Date | 2014-02-18 |
| License | GPL (>= 2) |
| URL | http://www.spatstat.org |
| LazyData | true |
| NeedsCompilation | yes |
| ByteCompile | true |
| Packaged | 2014-02-18 05:14:55 UTC; adrian |
| Repository | CRAN |
| Date/Publication | 2014-02-18 08:44:46 |
| imports | abind , deldir (>= 0.0-21) , mgcv , polyclip (>= 1.2-0) , tensor |
| depends | base (>= 3.0.2) , graphics , grDevices , R (>= 3.0.2) , stats , utils |
| suggests | gsl , locfit , maptools , RandomFields (>= 3.0.0) , rpanel , scatterplot3d , sm , spatial , tkrplot |
| Contributors | Rolf Turner, Adrian Baddeley |
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