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A B C D E F G H I J K L M N O P Q R S T U V W Z misc
| spatstat.core-package | The spatstat.core Package |
| adaptive.density | Adaptive Estimate of Intensity of Point Pattern |
| addvar | Added Variable Plot for Point Process Model |
| AIC.dppm | Log Likelihood and AIC for Fitted Determinantal Point Process Model |
| AIC.kppm | Log Likelihood and AIC for Fitted Cox or Cluster Point Process Model |
| AIC.mppm | Log Likelihood and AIC for Multiple Point Process Model |
| AIC.ppm | Log Likelihood and AIC for Point Process Model |
| allstats | Calculate four standard summary functions of a point pattern. |
| alltypes | Calculate Summary Statistic for All Types in a Multitype Point Pattern |
| anova.mppm | ANOVA for Fitted Point Process Models for Replicated Patterns |
| anova.ppm | ANOVA for Fitted Point Process Models |
| anova.slrm | Analysis of Deviance for Spatial Logistic Regression Models |
| apply.ssf | Evaluate Expression in a Spatially Sampled Function |
| AreaInter | The Area Interaction Point Process Model |
| as.data.frame.envelope | Coerce Envelope to Data Frame |
| as.function.fv | Convert Function Value Table to Function |
| as.function.leverage.ppm | Convert Leverage Object to Function of Coordinates |
| as.function.rhohat | Convert Function Table to Function |
| as.function.ssf | Methods for Spatially Sampled Functions |
| as.fv | Convert Data To Class fv |
| as.fv.bw.optim | Convert Data To Class fv |
| as.fv.data.frame | Convert Data To Class fv |
| as.fv.dppm | Convert Data To Class fv |
| as.fv.fasp | Convert Data To Class fv |
| as.fv.fv | Convert Data To Class fv |
| as.fv.kppm | Convert Data To Class fv |
| as.fv.matrix | Convert Data To Class fv |
| as.fv.minconfit | Convert Data To Class fv |
| as.im.leverage.ppm | Methods for Leverage Objects |
| as.im.scan.test | Plot Result of Scan Test |
| as.im.ssf | Methods for Spatially Sampled Functions |
| as.interact | Extract Interaction Structure |
| as.interact.fii | Extract Interaction Structure |
| as.interact.interact | Extract Interaction Structure |
| as.interact.ppm | Extract Interaction Structure |
| as.interact.zgibbsmodel | Methods for Gibbs Models |
| as.isf.zgibbsmodel | Methods for Gibbs Models |
| as.layered.msr | Convert Measure To Layered Object |
| as.owin.dppm | Convert Data To Class owin |
| as.owin.influence.ppm | Methods for Influence Objects |
| as.owin.kppm | Convert Data To Class owin |
| as.owin.leverage.ppm | Methods for Leverage Objects |
| as.owin.msr | Convert Data To Class owin |
| as.owin.ppm | Convert Data To Class owin |
| as.owin.quadrattest | Convert Data To Class owin |
| as.owin.slrm | Convert Data To Class owin |
| as.ppm | Extract Fitted Point Process Model |
| as.ppm.dppm | Extract Fitted Point Process Model |
| as.ppm.kppm | Extract Fitted Point Process Model |
| as.ppm.ppm | Extract Fitted Point Process Model |
| as.ppm.profilepl | Extract Fitted Point Process Model |
| as.ppp.influence.ppm | Methods for Influence Objects |
| as.ppp.ssf | Methods for Spatially Sampled Functions |
| auc | Area Under ROC Curve |
| auc.kppm | Area Under ROC Curve |
| auc.ppm | Area Under ROC Curve |
| auc.ppp | Area Under ROC Curve |
| auc.slrm | Area Under ROC Curve |
| BadGey | Hybrid Geyer Point Process Model |
| bc | Bias Correction for Fitted Model |
| bc.ppm | Bias Correction for Fitted Model |
| berman.test | Berman's Tests for Point Process Model |
| berman.test.ppm | Berman's Tests for Point Process Model |
| berman.test.ppp | Berman's Tests for Point Process Model |
| bind.fv | Combine Function Value Tables |
| bits.envelope | Global Envelopes for Balanced Independent Two-Stage Test |
| bits.test | Balanced Independent Two-Stage Monte Carlo Test |
| blur | Apply Gaussian Blur to a Pixel Image |
| bw.abram | Abramson's Adaptive Bandwidths |
| bw.CvL | Cronie and van Lieshout's Criterion for Bandwidth Selection for Kernel Density |
| bw.CvLHeat | Bandwidth Selection for Diffusion Smoother by Cronie-van Lieshout Rule |
| bw.diggle | Cross Validated Bandwidth Selection for Kernel Density |
| bw.frac | Bandwidth Selection Based on Window Geometry |
| bw.pcf | Cross Validated Bandwidth Selection for Pair Correlation Function |
| bw.ppl | Likelihood Cross Validation Bandwidth Selection for Kernel Density |
| bw.pplHeat | Bandwidth Selection for Diffusion Smoother by Likelihood Cross-Validation |
| bw.relrisk | Cross Validated Bandwidth Selection for Relative Risk Estimation |
| bw.scott | Scott's Rule for Bandwidth Selection for Kernel Density |
| bw.scott.iso | Scott's Rule for Bandwidth Selection for Kernel Density |
| bw.smoothppp | Cross Validated Bandwidth Selection for Spatial Smoothing |
| bw.stoyan | Stoyan's Rule of Thumb for Bandwidth Selection |
| cauchy.estK | Fit the Neyman-Scott cluster process with Cauchy kernel |
| cauchy.estpcf | Fit the Neyman-Scott cluster process with Cauchy kernel |
| cbind.fv | Combine Function Value Tables |
| CDF | Cumulative Distribution Function From Kernel Density Estimate |
| CDF.density | Cumulative Distribution Function From Kernel Density Estimate |
| cdf.test | Spatial Distribution Test for Point Pattern or Point Process Model |
| cdf.test.mppm | Spatial Distribution Test for Multiple Point Process Model |
| cdf.test.ppm | Spatial Distribution Test for Point Pattern or Point Process Model |
| cdf.test.ppp | Spatial Distribution Test for Point Pattern or Point Process Model |
| cdf.test.slrm | Spatial Distribution Test for Point Pattern or Point Process Model |
| circdensity | Density Estimation for Circular Data |
| clarkevans | Clark and Evans Aggregation Index |
| clarkevans.test | Clark and Evans Test |
| closepaircounts | Count Close Pairs of Points |
| clusterfield.kppm | Field of clusters |
| clusterfit | Fit Cluster or Cox Point Process Model via Minimum Contrast |
| clusterkernel.kppm | Extract Cluster Offspring Kernel |
| clusterradius.kppm | Compute or Extract Effective Range of Cluster Kernel |
| clusterradius.zclustermodel | Methods for Cluster Models |
| clusterset | Allard-Fraley Estimator of Cluster Feature |
| coef.dppm | Methods for Determinantal Point Process Models |
| coef.fii | Methods for Fitted Interactions |
| coef.kppm | Methods for Cluster Point Process Models |
| coef.mppm | Coefficients of Point Process Model Fitted to Multiple Point Patterns |
| coef.ppm | Coefficients of Fitted Point Process Model |
| coef.slrm | Coefficients of Fitted Spatial Logistic Regression Model |
| coef.summary.fii | Methods for Fitted Interactions |
| coef<-.fii | Methods for Fitted Interactions |
| collapse.anylist | Collapse Several Function Tables into One |
| collapse.fv | Collapse Several Function Tables into One |
| compareFit | Residual Diagnostics for Multiple Fitted Models |
| compatible.fasp | Test Whether Function Arrays Are Compatible |
| compatible.fv | Test Whether Function Objects Are Compatible |
| compileK | Generic Calculation of K Function and Pair Correlation Function |
| compilepcf | Generic Calculation of K Function and Pair Correlation Function |
| Concom | The Connected Component Process Model |
| contour.leverage.ppm | Plot Leverage Function |
| contour.objsurf | Methods for Objective Function Surfaces |
| contour.ssf | Plot a Spatially Sampled Function |
| cor.im | Covariance and Correlation between Images |
| cov.im | Covariance and Correlation between Images |
| crosspaircounts | Count Close Pairs of Points |
| data.ppm | Extract Original Data from a Fitted Point Process Model |
| dclf.progress | Progress Plot of Test of Spatial Pattern |
| dclf.sigtrace | Significance Trace of Cressie-Loosmore-Ford or Maximum Absolute Deviation Test |
| dclf.test | Diggle-Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests |
| density.ppp | Kernel Smoothed Intensity of Point Pattern |
| density.ppplist | Kernel Smoothed Intensity of Split Point Pattern |
| density.psp | Kernel Smoothing of Line Segment Pattern |
| density.splitppp | Kernel Smoothed Intensity of Split Point Pattern |
| densityAdaptiveKernel | Adaptive Kernel Estimate of Intensity of Point Pattern |
| densityAdaptiveKernel.ppp | Adaptive Kernel Estimate of Intensity of Point Pattern |
| densityfun | Kernel Estimate of Intensity as a Spatial Function |
| densityfun.ppp | Kernel Estimate of Intensity as a Spatial Function |
| densityHeat | Diffusion Estimate of Point Pattern Intensity |
| densityHeat.ppp | Diffusion Estimate of Point Pattern Intensity |
| densityVoronoi | Intensity Estimate of Point Pattern Using Voronoi-Dirichlet Tessellation |
| densityVoronoi.ppp | Intensity Estimate of Point Pattern Using Voronoi-Dirichlet Tessellation |
| deriv.fv | Calculate Derivative of Function Values |
| detpointprocfamilyfun | Construct a New Determinantal Point Process Model Family Function |
| deviance.ppm | Log Likelihood and AIC for Point Process Model |
| deviance.slrm | Methods for Spatial Logistic Regression Models |
| dfbetas.ppm | Parameter Influence Measure |
| dfbetas.slrm | Leverage and Influence Diagnostics for Spatial Logistic Regression |
| dffit | Case Deletion Effect Measure of Fitted Model |
| dffit.ppm | Case Deletion Effect Measure of Fitted Model |
| dffit.slrm | Leverage and Influence Diagnostics for Spatial Logistic Regression |
| dg.envelope | Global Envelopes for Dao-Genton Test |
| dg.progress | Progress Plot of Dao-Genton Test of Spatial Pattern |
| dg.sigtrace | Significance Trace of Dao-Genton Test |
| dg.test | Dao-Genton Adjusted Goodness-Of-Fit Test |
| diagnose.ppm | Diagnostic Plots for Fitted Point Process Model |
| DiggleGatesStibbard | Diggle-Gates-Stibbard Point Process Model |
| DiggleGratton | Diggle-Gratton model |
| dim.detpointprocfamily | Dimension of Determinantal Point Process Model |
| dimhat | Estimate Dimension of Central Subspace |
| distcdf | Distribution Function of Interpoint Distance |
| dkernel | Kernel distributions and random generation |
| domain.dppm | Extract the Domain of any Spatial Object |
| domain.influence.ppm | Methods for Influence Objects |
| domain.kppm | Extract the Domain of any Spatial Object |
| domain.leverage.ppm | Methods for Leverage Objects |
| domain.msr | Extract the Domain of any Spatial Object |
| domain.ppm | Extract the Domain of any Spatial Object |
| domain.quadrattest | Extract the Domain of any Spatial Object |
| domain.slrm | Extract the Domain of any Spatial Object |
| dppapproxkernel | Approximate Determinantal Point Process Kernel |
| dppapproxpcf | Approximate Pair Correlation Function of Determinantal Point Process Model |
| dppBessel | Bessel Type Determinantal Point Process Model |
| dppCauchy | Generalized Cauchy Determinantal Point Process Model |
| dppeigen | Internal function calculating eig and index |
| dppGauss | Gaussian Determinantal Point Process Model |
| dppkernel | Extract Kernel from Determinantal Point Process Model Object |
| dppm | Fit Determinantal Point Process Model |
| dppMatern | Whittle-Matern Determinantal Point Process Model |
| dppparbounds | Parameter Bound for a Determinantal Point Process Model |
| dppPowerExp | Power Exponential Spectral Determinantal Point Process Model |
| dppspecden | Extract Spectral Density from Determinantal Point Process Model Object |
| dppspecdenrange | Range of Spectral Density of a Determinantal Point Process Model |
| dummify | Convert Data to Numeric Values by Constructing Dummy Variables |
| dummy.ppm | Extract Dummy Points Used to Fit a Point Process Model |
| edge.Ripley | Ripley's Isotropic Edge Correction |
| edge.Trans | Translation Edge Correction |
| eem | Exponential Energy Marks |
| eem.ppm | Exponential Energy Marks |
| eem.slrm | Exponential Energy Marks |
| effectfun | Compute Fitted Effect of a Spatial Covariate in a Point Process Model |
| Emark | Diagnostics for random marking |
| emend | Force Model to be Valid |
| emend.ppm | Force Point Process Model to be Valid |
| emend.slrm | Force Spatial Logistic Regression Model to be Valid |
| envelope | Simulation Envelopes of Summary Function |
| envelope.envelope | Recompute Envelopes |
| envelope.kppm | Simulation Envelopes of Summary Function |
| envelope.pp3 | Simulation Envelopes of Summary Function for 3D Point Pattern |
| envelope.ppm | Simulation Envelopes of Summary Function |
| envelope.ppp | Simulation Envelopes of Summary Function |
| envelope.slrm | Simulation Envelopes of Summary Function |
| envelopeArray | Array of Simulation Envelopes of Summary Function |
| eval.fasp | Evaluate Expression Involving Function Arrays |
| eval.fv | Evaluate Expression Involving Functions |
| exactMPLEstrauss | Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process |
| extractAIC.dppm | Log Likelihood and AIC for Fitted Determinantal Point Process Model |
| extractAIC.kppm | Log Likelihood and AIC for Fitted Cox or Cluster Point Process Model |
| extractAIC.mppm | Log Likelihood and AIC for Multiple Point Process Model |
| extractAIC.ppm | Log Likelihood and AIC for Point Process Model |
| F3est | Empty Space Function of a Three-Dimensional Point Pattern |
| fasp.object | Function Arrays for Spatial Patterns |
| Fest | Estimate the Empty Space Function or its Hazard Rate |
| Fhazard | Estimate the Empty Space Function or its Hazard Rate |
| Fiksel | The Fiksel Interaction |
| Finhom | Inhomogeneous Empty Space Function |
| fitin | Extract the Interaction from a Fitted Point Process Model |
| fitin.ppm | Extract the Interaction from a Fitted Point Process Model |
| fitin.profilepl | Extract the Interaction from a Fitted Point Process Model |
| fitted.dppm | Prediction from a Fitted Determinantal Point Process Model |
| fitted.kppm | Prediction from a Fitted Cluster Point Process Model |
| fitted.mppm | Fitted Conditional Intensity for Multiple Point Process Model |
| fitted.ppm | Fitted Conditional Intensity for Point Process Model |
| fitted.rppm | Make Predictions From a Recursively Partitioned Point Process Model |
| fitted.slrm | Fitted Probabilities for Spatial Logistic Regression |
| fixef.mppm | Extract Fixed Effects from Point Process Model |
| FmultiInhom | Inhomogeneous Marked F-Function |
| formula.dppm | Methods for Determinantal Point Process Models |
| formula.fv | Extract or Change the Plot Formula for a Function Value Table |
| formula.kppm | Methods for Cluster Point Process Models |
| formula.ppm | Model Formulae for Gibbs Point Process Models |
| formula.slrm | Methods for Spatial Logistic Regression Models |
| formula<- | Extract or Change the Plot Formula for a Function Value Table |
| formula<-.fv | Extract or Change the Plot Formula for a Function Value Table |
| fryplot | Fry Plot of Point Pattern |
| frypoints | Fry Plot of Point Pattern |
| fv | Create a Function Value Table |
| fv.object | Function Value Table |
| fvnames | Abbreviations for Groups of Columns in Function Value Table |
| fvnames<- | Abbreviations for Groups of Columns in Function Value Table |
| G3est | Nearest Neighbour Distance Distribution Function of a Three-Dimensional Point Pattern |
| Gcom | Model Compensator of Nearest Neighbour Function |
| Gcross | Multitype Nearest Neighbour Distance Function (i-to-j) |
| Gdot | Multitype Nearest Neighbour Distance Function (i-to-any) |
| Gest | Nearest Neighbour Distance Function G |
| getCall.mppm | Log Likelihood and AIC for Multiple Point Process Model |
| Geyer | Geyer's Saturation Point Process Model |
| Gfox | Foxall's Distance Functions |
| Ginhom | Inhomogeneous Nearest Neighbour Function |
| Gmulti | Marked Nearest Neighbour Distance Function |
| GmultiInhom | Inhomogeneous Marked G-Function |
| Gres | Residual G Function |
| Hardcore | The Hard Core Point Process Model |
| hardcoredist | Extract the Hard Core Distance of a Point Process Model |
| hardcoredist.fii | Extract the Hard Core Distance of a Point Process Model |
| hardcoredist.ppm | Extract the Hard Core Distance of a Point Process Model |
| harmonic | Basis for Harmonic Functions |
| harmonise.fv | Make Function Tables Compatible |
| harmonise.msr | Make Measures Compatible |
| harmonize.fv | Make Function Tables Compatible |
| has.offset | Identify Covariates Involved in each Model Term |
| has.offset.term | Identify Covariates Involved in each Model Term |
| Hest | Spherical Contact Distribution Function |
| HierHard | The Hierarchical Hard Core Point Process Model |
| hierpair.family | Hierarchical Pairwise Interaction Process Family |
| HierStrauss | The Hierarchical Strauss Point Process Model |
| HierStraussHard | The Hierarchical Strauss Hard Core Point Process Model |
| hopskel | Hopkins-Skellam Test |
| hopskel.test | Hopkins-Skellam Test |
| hotbox | Heat Kernel for a Two-Dimensional Rectangle |
| Hybrid | Hybrid Interaction Point Process Model |
| hybrid.family | Hybrid Interaction Family |
| ic | Model selection criteria for the intensity function of a point process |
| ic.kppm | Model selection criteria for the intensity function of a point process |
| ic.ppm | Model selection criteria for the intensity function of a point process |
| idw | Inverse-distance weighted smoothing of observations at irregular points |
| Iest | Estimate the I-function |
| image.objsurf | Methods for Objective Function Surfaces |
| image.ssf | Plot a Spatially Sampled Function |
| improve.kppm | Improve Intensity Estimate of Fitted Cluster Point Process Model |
| increment.fv | Increments of a Function |
| influence.ppm | Influence Measure for Spatial Point Process Model |
| influence.slrm | Leverage and Influence Diagnostics for Spatial Logistic Regression |
| inforder.family | Infinite Order Interaction Family |
| integral.influence.ppm | Methods for Influence Objects |
| integral.leverage.ppm | Methods for Leverage Objects |
| integral.msr | Integral of a Measure |
| integral.ssf | Methods for Spatially Sampled Functions |
| intensity.detpointprocfamily | Intensity of Determinantal Point Process Model |
| intensity.dppm | Intensity of Determinantal Point Process Model |
| intensity.ppm | Intensity of Fitted Point Process Model |
| intensity.slrm | Intensity of Fitted Spatial Logistic Regression Model |
| intensity.zclustermodel | Methods for Cluster Models |
| intensity.zgibbsmodel | Methods for Gibbs Models |
| interactionorder | Determine the Order of Interpoint Interaction in a Model |
| interactionorder.fii | Determine the Order of Interpoint Interaction in a Model |
| interactionorder.interact | Determine the Order of Interpoint Interaction in a Model |
| interactionorder.isf | Determine the Order of Interpoint Interaction in a Model |
| interactionorder.ppm | Determine the Order of Interpoint Interaction in a Model |
| interactionorder.zgibbsmodel | Methods for Gibbs Models |
| ippm | Fit Point Process Model Involving Irregular Trend Parameters |
| is.dppm | Recognise Fitted Determinantal Point Process Models |
| is.hybrid | Test Whether Object is a Hybrid |
| is.hybrid.interact | Test Whether Object is a Hybrid |
| is.hybrid.ppm | Test Whether Object is a Hybrid |
| is.kppm | Test Whether An Object Is A Fitted Point Process Model |
| is.lppm | Test Whether An Object Is A Fitted Point Process Model |
| is.marked.ppm | Test Whether A Point Process Model is Marked |
| is.multitype.ppm | Test Whether A Point Process Model is Multitype |
| is.poisson.interact | Recognise Stationary and Poisson Point Process Models |
| is.poisson.kppm | Recognise Stationary and Poisson Point Process Models |
| is.poisson.ppm | Recognise Stationary and Poisson Point Process Models |
| is.poisson.slrm | Recognise Stationary and Poisson Point Process Models |
| is.poisson.zgibbsmodel | Methods for Gibbs Models |
| is.ppm | Test Whether An Object Is A Fitted Point Process Model |
| is.slrm | Test Whether An Object Is A Fitted Point Process Model |
| is.stationary.detpointprocfamily | Recognise Stationary and Poisson Point Process Models |
| is.stationary.dppm | Recognise Stationary and Poisson Point Process Models |
| is.stationary.kppm | Recognise Stationary and Poisson Point Process Models |
| is.stationary.ppm | Recognise Stationary and Poisson Point Process Models |
| is.stationary.slrm | Recognise Stationary and Poisson Point Process Models |
| is.stationary.zgibbsmodel | Methods for Gibbs Models |
| isf.object | Interaction Structure Family Objects |
| Jcross | Multitype J Function (i-to-j) |
| Jdot | Multitype J Function (i-to-any) |
| Jest | Estimate the J-function |
| Jfox | Foxall's Distance Functions |
| Jinhom | Inhomogeneous J-function |
| Jmulti | Marked J Function |
| K3est | K-function of a Three-Dimensional Point Pattern |
| kaplan.meier | Kaplan-Meier Estimator using Histogram Data |
| Kcom | Model Compensator of K Function |
| Kcross | Multitype K Function (Cross-type) |
| Kcross.inhom | Inhomogeneous Cross K Function |
| Kdot | Multitype K Function (i-to-any) |
| Kdot.inhom | Inhomogeneous Multitype K Dot Function |
| kernel.factor | Scale factor for density kernel |
| kernel.moment | Moment of Smoothing Kernel |
| kernel.squint | Integral of Squared Kernel |
| Kest | K-function |
| Kest.fft | K-function using FFT |
| Kinhom | Inhomogeneous K-function |
| km.rs | Kaplan-Meier and Reduced Sample Estimator using Histograms |
| Kmark | Mark-Weighted K Function |
| Kmeasure | Reduced Second Moment Measure |
| Kmodel | K Function or Pair Correlation Function of a Point Process Model |
| Kmodel.detpointprocfamily | K-function or Pair Correlation Function of a Determinantal Point Process Model |
| Kmodel.dppm | K-function or Pair Correlation Function of a Determinantal Point Process Model |
| Kmodel.kppm | K Function or Pair Correlation Function of Cluster Model or Cox model |
| Kmodel.ppm | K Function or Pair Correlation Function of Gibbs Point Process model |
| Kmodel.zclustermodel | Methods for Cluster Models |
| Kmulti | Marked K-Function |
| Kmulti.inhom | Inhomogeneous Marked K-Function |
| kppm | Fit Cluster or Cox Point Process Model |
| kppm.formula | Fit Cluster or Cox Point Process Model |
| kppm.ppp | Fit Cluster or Cox Point Process Model |
| kppm.quad | Fit Cluster or Cox Point Process Model |
| Kres | Residual K Function |
| Kscaled | Locally Scaled K-function |
| Ksector | Sector K-function |
| labels.dppm | Methods for Determinantal Point Process Models |
| labels.kppm | Methods for Cluster Point Process Models |
| labels.slrm | Methods for Spatial Logistic Regression Models |
| LambertW | Lambert's W Function |
| laslett | Laslett's Transform |
| Lcross | Multitype L-function (cross-type) |
| Lcross.inhom | Inhomogeneous Cross Type L Function |
| Ldot | Multitype L-function (i-to-any) |
| Ldot.inhom | Inhomogeneous Multitype L Dot Function |
| LennardJones | The Lennard-Jones Potential |
| Lest | L-function |
| leverage | Leverage Measure for Spatial Point Process Model |
| leverage.ppm | Leverage Measure for Spatial Point Process Model |
| leverage.slrm | Leverage and Influence Diagnostics for Spatial Logistic Regression |
| lgcp.estK | Fit a Log-Gaussian Cox Point Process by Minimum Contrast |
| lgcp.estpcf | Fit a Log-Gaussian Cox Point Process by Minimum Contrast |
| Linhom | Inhomogeneous L-function |
| localK | Neighbourhood density function |
| localKcross | Local Multitype K Function (Cross-Type) |
| localKcross.inhom | Inhomogeneous Multitype K Function |
| localKdot | Local Multitype K Function (Dot-Type) |
| localKinhom | Inhomogeneous Neighbourhood Density Function |
| localL | Neighbourhood density function |
| localLcross | Local Multitype K Function (Cross-Type) |
| localLcross.inhom | Inhomogeneous Multitype K Function |
| localLdot | Local Multitype K Function (Dot-Type) |
| localLinhom | Inhomogeneous Neighbourhood Density Function |
| localpcf | Local pair correlation function |
| localpcfinhom | Local pair correlation function |
| logLik.dppm | Log Likelihood and AIC for Fitted Determinantal Point Process Model |
| logLik.kppm | Log Likelihood and AIC for Fitted Cox or Cluster Point Process Model |
| logLik.mppm | Log Likelihood and AIC for Multiple Point Process Model |
| logLik.ppm | Log Likelihood and AIC for Point Process Model |
| logLik.slrm | Loglikelihood of Spatial Logistic Regression |
| lohboot | Bootstrap Confidence Bands for Summary Function |
| Lscaled | Locally Scaled K-function |
| lurking | Lurking Variable Plot |
| lurking.mppm | Lurking Variable Plot for Multiple Point Patterns |
| lurking.ppm | Lurking Variable Plot |
| lurking.ppp | Lurking Variable Plot |
| mad.progress | Progress Plot of Test of Spatial Pattern |
| mad.sigtrace | Significance Trace of Cressie-Loosmore-Ford or Maximum Absolute Deviation Test |
| mad.test | Diggle-Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests |
| markconnect | Mark Connection Function |
| markcorr | Mark Correlation Function |
| markcorrint | Mark-Weighted K Function |
| markcrosscorr | Mark Cross-Correlation Function |
| markmarkscatter | Mark-Mark Scatter Plot |
| markmean | Spatial smoothing of observations at irregular points |
| marks.ssf | Methods for Spatially Sampled Functions |
| marks<-.ssf | Methods for Spatially Sampled Functions |
| marktable | Tabulate Marks in Neighbourhood of Every Point in a Point Pattern |
| markvar | Spatial smoothing of observations at irregular points |
| markvario | Mark Variogram |
| matclust.estK | Fit the Matern Cluster Point Process by Minimum Contrast |
| matclust.estpcf | Fit the Matern Cluster Point Process by Minimum Contrast Using Pair Correlation |
| max.fv | Range of Function Values |
| max.ssf | Methods for Spatially Sampled Functions |
| mctest.progress | Progress Plot of Test of Spatial Pattern |
| mctest.sigtrace | Significance Trace of Cressie-Loosmore-Ford or Maximum Absolute Deviation Test |
| mean.leverage.ppm | Methods for Leverage Objects |
| measureContinuous | Discrete and Continuous Components of a Measure |
| measureDiscrete | Discrete and Continuous Components of a Measure |
| measureNegative | Positive and Negative Parts, and Variation, of a Measure |
| measurePositive | Positive and Negative Parts, and Variation, of a Measure |
| measureVariation | Positive and Negative Parts, and Variation, of a Measure |
| methods.dppm | Methods for Determinantal Point Process Models |
| methods.fii | Methods for Fitted Interactions |
| methods.influence.ppm | Methods for Influence Objects |
| methods.kppm | Methods for Cluster Point Process Models |
| methods.leverage.ppm | Methods for Leverage Objects |
| methods.objsurf | Methods for Objective Function Surfaces |
| methods.ppm | Class of Fitted Point Process Models |
| methods.rho2hat | Methods for Intensity Functions of Two Spatial Covariates |
| methods.rhohat | Methods for Intensity Functions of Spatial Covariate |
| methods.slrm | Methods for Spatial Logistic Regression Models |
| methods.ssf | Methods for Spatially Sampled Functions |
| methods.zclustermodel | Methods for Cluster Models |
| methods.zgibbsmodel | Methods for Gibbs Models |
| min.fv | Range of Function Values |
| min.ssf | Methods for Spatially Sampled Functions |
| mincontrast | Method of Minimum Contrast |
| miplot | Morisita Index Plot |
| model.covariates | Identify Covariates Involved in each Model Term |
| model.depends | Identify Covariates Involved in each Model Term |
| model.frame.dppm | Extract the Variables in a Point Process Model |
| model.frame.kppm | Extract the Variables in a Point Process Model |
| model.frame.ppm | Extract the Variables in a Point Process Model |
| model.frame.slrm | Extract the Variables in a Point Process Model |
| model.images | Compute Images of Constructed Covariates |
| model.images.dppm | Compute Images of Constructed Covariates |
| model.images.kppm | Compute Images of Constructed Covariates |
| model.images.ppm | Compute Images of Constructed Covariates |
| model.images.slrm | Compute Images of Constructed Covariates |
| model.is.additive | Identify Covariates Involved in each Model Term |
| model.matrix.dppm | Extract Design Matrix from Point Process Model |
| model.matrix.ippm | Extract Design Matrix from Point Process Model |
| model.matrix.kppm | Extract Design Matrix from Point Process Model |
| model.matrix.mppm | Extract Design Matrix of Point Process Model for Several Point Patterns |
| model.matrix.ppm | Extract Design Matrix from Point Process Model |
| model.matrix.slrm | Extract Design Matrix from Spatial Logistic Regression Model |
| mppm | Fit Point Process Model to Several Point Patterns |
| msr | Signed or Vector-Valued Measure |
| MultiHard | The Multitype Hard Core Point Process Model |
| MultiStrauss | The Multitype Strauss Point Process Model |
| MultiStraussHard | The Multitype/Hard Core Strauss Point Process Model |
| nearest.neighbour | Nearest Neighbour Distance Function G |
| nnclean | Nearest Neighbour Clutter Removal |
| nnclean.pp3 | Nearest Neighbour Clutter Removal |
| nnclean.ppp | Nearest Neighbour Clutter Removal |
| nncorr | Nearest-Neighbour Correlation Indices of Marked Point Pattern |
| nndensity | Estimate Intensity of Point Pattern Using Nearest Neighbour Distances |
| nndensity.ppp | Estimate Intensity of Point Pattern Using Nearest Neighbour Distances |
| nnmean | Nearest-Neighbour Correlation Indices of Marked Point Pattern |
| nnorient | Nearest Neighbour Orientation Distribution |
| nnvario | Nearest-Neighbour Correlation Indices of Marked Point Pattern |
| nobs.dppm | Log Likelihood and AIC for Fitted Determinantal Point Process Model |
| nobs.kppm | Log Likelihood and AIC for Fitted Cox or Cluster Point Process Model |
| nobs.mppm | Log Likelihood and AIC for Multiple Point Process Model |
| nobs.ppm | Log Likelihood and AIC for Point Process Model |
| npfun | Dummy Function Returns Number of Points |
| objsurf | Objective Function Surface |
| objsurf.dppm | Objective Function Surface |
| objsurf.kppm | Objective Function Surface |
| objsurf.minconfit | Objective Function Surface |
| Ops.msr | Arithmetic Operations on Measures |
| Ord | Generic Ord Interaction model |
| ord.family | Ord Interaction Process Family |
| OrdThresh | Ord's Interaction model |
| pairMean | Mean of a Function of Interpoint Distance |
| pairorient | Point Pair Orientation Distribution |
| PairPiece | The Piecewise Constant Pairwise Interaction Point Process Model |
| pairs.im | Scatterplot Matrix for Pixel Images |
| pairsat.family | Saturated Pairwise Interaction Point Process Family |
| Pairwise | Generic Pairwise Interaction model |
| pairwise.family | Pairwise Interaction Process Family |
| panel.contour | Panel Plots using Colour Image or Contour Lines |
| panel.histogram | Panel Plots using Colour Image or Contour Lines |
| panel.image | Panel Plots using Colour Image or Contour Lines |
| parameters | Extract Model Parameters in Understandable Form |
| parameters.dppm | Extract Model Parameters in Understandable Form |
| parameters.fii | Extract Model Parameters in Understandable Form |
| parameters.interact | Extract Model Parameters in Understandable Form |
| parameters.kppm | Extract Model Parameters in Understandable Form |
| parameters.ppm | Extract Model Parameters in Understandable Form |
| parameters.profilepl | Extract Model Parameters in Understandable Form |
| parameters.slrm | Extract Model Parameters in Understandable Form |
| parres | Partial Residuals for Point Process Model |
| pcf | Pair Correlation Function |
| pcf.fasp | Pair Correlation Function obtained from array of K functions |
| pcf.fv | Pair Correlation Function obtained from K Function |
| pcf.ppp | Pair Correlation Function of Point Pattern |
| pcf3est | Pair Correlation Function of a Three-Dimensional Point Pattern |
| pcfcross | Multitype pair correlation function (cross-type) |
| pcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-Type) |
| pcfdot | Multitype pair correlation function (i-to-any) |
| pcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Type-i-To-Any-Type) |
| pcfinhom | Inhomogeneous Pair Correlation Function |
| pcfmodel | K Function or Pair Correlation Function of a Point Process Model |
| pcfmodel.detpointprocfamily | K-function or Pair Correlation Function of a Determinantal Point Process Model |
| pcfmodel.dppm | K-function or Pair Correlation Function of a Determinantal Point Process Model |
| pcfmodel.kppm | K Function or Pair Correlation Function of Cluster Model or Cox model |
| pcfmodel.ppm | K Function or Pair Correlation Function of Gibbs Point Process model |
| pcfmodel.zclustermodel | Methods for Cluster Models |
| pcfmulti | Marked pair correlation function |
| Penttinen | Penttinen Interaction |
| persp.leverage.ppm | Plot Leverage Function |
| persp.objsurf | Methods for Objective Function Surfaces |
| pkernel | Kernel distributions and random generation |
| plot.bermantest | Plot Result of Berman Test |
| plot.cdftest | Plot a Spatial Distribution Test |
| plot.diagppm | Diagnostic Plots for Fitted Point Process Model |
| plot.dppm | Plot a fitted determinantal point process |
| plot.envelope | Plot a Simulation Envelope |
| plot.fasp | Plot a Function Array |
| plot.fii | Methods for Fitted Interactions |
| plot.fv | Plot Function Values |
| plot.influence.ppm | Plot Influence Measure |
| plot.kppm | Plot a fitted cluster point process |
| plot.laslett | Plot Laslett Transform |
| plot.leverage.ppm | Plot Leverage Function |
| plot.mppm | plot a Fitted Multiple Point Process Model |
| plot.msr | Plot a Signed or Vector-Valued Measure |
| plot.objsurf | Methods for Objective Function Surfaces |
| plot.plotppm | Plot a plotppm Object Created by plot.ppm |
| plot.ppm | plot a Fitted Point Process Model |
| plot.profilepl | Plot Profile Likelihood |
| plot.quadrattest | Display the result of a quadrat counting test. |
| plot.rho2hat | Methods for Intensity Functions of Two Spatial Covariates |
| plot.rhohat | Methods for Intensity Functions of Spatial Covariate |
| plot.rppm | Plot a Recursively Partitioned Point Process Model |
| plot.scan.test | Plot Result of Scan Test |
| plot.slrm | Plot a Fitted Spatial Logistic Regression |
| plot.ssf | Plot a Spatially Sampled Function |
| plot.studpermutest | Plot a Studentised Permutation Test |
| Poisson | Poisson Point Process Model |
| polynom | Polynomial in One or Two Variables |
| pool | Pool Data |
| pool.anylist | Pool Data from a List of Objects |
| pool.envelope | Pool Data from Several Envelopes |
| pool.fasp | Pool Data from Several Function Arrays |
| pool.fv | Pool Several Functions |
| pool.quadrattest | Pool Several Quadrat Tests |
| pool.rat | Pool Data from Several Ratio Objects |
| ppm | Fit Point Process Model to Data |
| ppm.formula | Fit Point Process Model to Data |
| ppm.object | Class of Fitted Point Process Models |
| ppm.ppp | Fit Point Process Model to Point Pattern Data |
| ppm.quad | Fit Point Process Model to Point Pattern Data |
| ppmInfluence | Leverage and Influence Measures for Spatial Point Process Model |
| PPversion | Transform a Function into its P-P or Q-Q Version |
| predict.dppm | Prediction from a Fitted Determinantal Point Process Model |
| predict.kppm | Prediction from a Fitted Cluster Point Process Model |
| predict.mppm | Prediction for Fitted Multiple Point Process Model |
| predict.ppm | Prediction from a Fitted Point Process Model |
| predict.rho2hat | Methods for Intensity Functions of Two Spatial Covariates |
| predict.rhohat | Methods for Intensity Functions of Spatial Covariate |
| predict.rppm | Make Predictions From a Recursively Partitioned Point Process Model |
| predict.slrm | Predicted or Fitted Values from Spatial Logistic Regression |
| predict.zclustermodel | Methods for Cluster Models |
| print.dppm | Methods for Determinantal Point Process Models |
| print.fii | Methods for Fitted Interactions |
| print.kppm | Methods for Cluster Point Process Models |
| print.objsurf | Methods for Objective Function Surfaces |
| print.ppm | Print a Fitted Point Process Model |
| print.rho2hat | Methods for Intensity Functions of Two Spatial Covariates |
| print.rhohat | Methods for Intensity Functions of Spatial Covariate |
| print.slrm | Methods for Spatial Logistic Regression Models |
| print.ssf | Methods for Spatially Sampled Functions |
| print.summary.dppm | Summarizing a Fitted Determinantal Point Process Model |
| print.summary.fii | Methods for Fitted Interactions |
| print.summary.kppm | Summarizing a Fitted Cox or Cluster Point Process Model |
| print.summary.objsurf | Methods for Objective Function Surfaces |
| print.summary.ppm | Summarizing a Fitted Point Process Model |
| print.zclustermodel | Methods for Cluster Models |
| print.zgibbsmodel | Methods for Gibbs Models |
| profilepl | Fit Models by Profile Maximum Pseudolikelihood or AIC |
| project.ppm | Force Point Process Model to be Valid |
| prune.rppm | Prune a Recursively Partitioned Point Process Model |
| pseudoR2 | Calculate Pseudo-R-Squared for Point Process Model |
| pseudoR2.ppm | Calculate Pseudo-R-Squared for Point Process Model |
| pseudoR2.slrm | Calculate Pseudo-R-Squared for Point Process Model |
| psib | Sibling Probability of Cluster Point Process |
| psib.kppm | Sibling Probability of Cluster Point Process |
| psst | Pseudoscore Diagnostic For Fitted Model against General Alternative |
| psstA | Pseudoscore Diagnostic For Fitted Model against Area-Interaction Alternative |
| psstG | Pseudoscore Diagnostic For Fitted Model against Saturation Alternative |
| qkernel | Kernel distributions and random generation |
| qqplot.ppm | Q-Q Plot of Residuals from Fitted Point Process Model |
| QQversion | Transform a Function into its P-P or Q-Q Version |
| quad.ppm | Extract Quadrature Scheme Used to Fit a Point Process Model |
| quadrat.test | Dispersion Test for Spatial Point Pattern Based on Quadrat Counts |
| quadrat.test.mppm | Chi-Squared Test for Multiple Point Process Model Based on Quadrat Counts |
| quadrat.test.ppm | Dispersion Test for Spatial Point Pattern Based on Quadrat Counts |
| quadrat.test.ppp | Dispersion Test for Spatial Point Pattern Based on Quadrat Counts |
| quadrat.test.quadratcount | Dispersion Test for Spatial Point Pattern Based on Quadrat Counts |
| quadrat.test.slrm | Dispersion Test for Spatial Point Pattern Based on Quadrat Counts |
| quadrat.test.splitppp | Dispersion Test of CSR for Split Point Pattern Based on Quadrat Counts |
| quantile.density | Quantiles of a Density Estimate |
| ranef.mppm | Extract Random Effects from Point Process Model |
| range.fv | Range of Function Values |
| range.ssf | Methods for Spatially Sampled Functions |
| rat | Ratio object |
| rdpp | Simulation of a Determinantal Point Process |
| reach.detpointprocfamily | Range of Interaction for a Determinantal Point Process Model |
| reach.dppm | Range of Interaction for a Determinantal Point Process Model |
| reach.fii | Interaction Distance of a Point Process Model |
| reach.interact | Interaction Distance of a Point Process Model |
| reach.kppm | Range of Interaction for a Cox or Cluster Point Process Model |
| reach.ppm | Interaction Distance of a Point Process Model |
| reach.zclustermodel | Methods for Cluster Models |
| rectcontact | Contact Distribution Function using Rectangular Structuring Element |
| reduced.sample | Reduced Sample Estimator using Histogram Data |
| reload.or.compute | Compute Unless Previously Saved |
| relrisk | Estimate of Spatially-Varying Relative Risk |
| relrisk.ppm | Parametric Estimate of Spatially-Varying Relative Risk |
| relrisk.ppp | Nonparametric Estimate of Spatially-Varying Relative Risk |
| repul | Repulsiveness Index of a Determinantal Point Process Model |
| repul.dppm | Repulsiveness Index of a Determinantal Point Process Model |
| residuals.dppm | Residuals for Fitted Determinantal Point Process Model |
| residuals.kppm | Residuals for Fitted Cox or Cluster Point Process Model |
| residuals.mppm | Residuals for Point Process Model Fitted to Multiple Point Patterns |
| residuals.ppm | Residuals for Fitted Point Process Model |
| residuals.slrm | Residuals for Fitted Spatial Logistic Regression Model |
| response | Extract the Values of the Response from a Fitted Model |
| response.dppm | Extract the Values of the Response from a Fitted Model |
| response.glm | Extract the Values of the Response from a Fitted Model |
| response.kppm | Extract the Values of the Response from a Fitted Model |
| response.lm | Extract the Values of the Response from a Fitted Model |
| response.mppm | Extract the Values of the Response from a Fitted Model |
| response.ppm | Extract the Values of the Response from a Fitted Model |
| response.slrm | Extract the Values of the Response from a Fitted Model |
| rex | Richardson Extrapolation |
| rho2hat | Smoothed Relative Density of Pairs of Covariate Values |
| rhohat | Nonparametric Estimate of Intensity as Function of a Covariate |
| rhohat.ppm | Nonparametric Estimate of Intensity as Function of a Covariate |
| rhohat.ppp | Nonparametric Estimate of Intensity as Function of a Covariate |
| rhohat.quad | Nonparametric Estimate of Intensity as Function of a Covariate |
| rhohat.slrm | Nonparametric Estimate of Intensity as Function of a Covariate |
| rkernel | Kernel distributions and random generation |
| rmax.Ripley | Ripley's Isotropic Edge Correction |
| rmax.Trans | Translation Edge Correction |
| rmh.ppm | Simulate from a Fitted Point Process Model |
| rmhmodel.ppm | Interpret Fitted Model for Metropolis-Hastings Simulation. |
| roc | Receiver Operating Characteristic |
| roc.kppm | Receiver Operating Characteristic |
| roc.ppm | Receiver Operating Characteristic |
| roc.ppp | Receiver Operating Characteristic |
| roc.slrm | Receiver Operating Characteristic |
| rose | Rose Diagram |
| rose.default | Rose Diagram |
| rose.density | Rose Diagram |
| rose.fv | Rose Diagram |
| rose.histogram | Rose Diagram |
| rotmean | Rotational Average of a Pixel Image |
| rppm | Recursively Partitioned Point Process Model |
| SatPiece | Piecewise Constant Saturated Pairwise Interaction Point Process Model |
| Saturated | Saturated Pairwise Interaction model |
| scan.test | Spatial Scan Test |
| scanLRTS | Likelihood Ratio Test Statistic for Scan Test |
| sdr | Sufficient Dimension Reduction |
| sdr.ppp | Sufficient Dimension Reduction |
| sdrPredict | Compute Predictors from Sufficient Dimension Reduction |
| segregation.test | Test of Spatial Segregation of Types |
| segregation.test.ppp | Test of Spatial Segregation of Types |
| sharpen | Data Sharpening of Point Pattern |
| sharpen.ppp | Data Sharpening of Point Pattern |
| simulate.detpointprocfamily | Simulation of Determinantal Point Process Model |
| simulate.dppm | Simulation of Determinantal Point Process Model |
| simulate.kppm | Simulate a Fitted Cluster Point Process Model |
| simulate.mppm | Simulate a Point Process Model Fitted to Several Point Patterns |
| simulate.ppm | Simulate a Fitted Gibbs Point Process Model |
| simulate.rhohat | Methods for Intensity Functions of Spatial Covariate |
| simulate.slrm | Simulate a Fitted Spatial Logistic Regression Model |
| slrm | Spatial Logistic Regression |
| Smooth | Spatial smoothing of data |
| Smooth.fv | Apply Smoothing to Function Values |
| Smooth.im | Apply Gaussian Blur to a Pixel Image |
| Smooth.influence.ppm | Methods for Influence Objects |
| Smooth.leverage.ppm | Methods for Leverage Objects |
| Smooth.msr | Smooth a Signed or Vector-Valued Measure |
| Smooth.ppp | Spatial smoothing of observations at irregular points |
| Smooth.ssf | Smooth a Spatially Sampled Function |
| Smoothfun | Smooth Interpolation of Marks as a Spatial Function |
| Smoothfun.ppp | Smooth Interpolation of Marks as a Spatial Function |
| Softcore | The Soft Core Point Process Model |
| spatcov | Estimate the Spatial Covariance Function of a Random Field |
| spatialcdf | Spatial Cumulative Distribution Function |
| spatstat.core | The spatstat.core Package |
| split.msr | Divide a Measure into Parts |
| ssf | Spatially Sampled Function |
| stieltjes | Compute Integral of Function Against Cumulative Distribution |
| stienen | Stienen Diagram |
| stienenSet | Stienen Diagram |
| Strauss | The Strauss Point Process Model |
| StraussHard | The Strauss / Hard Core Point Process Model |
| studpermu.test | Studentised Permutation Test |
| subfits | Extract List of Individual Point Process Models |
| subfits.new | Extract List of Individual Point Process Models |
| subfits.old | Extract List of Individual Point Process Models |
| subspaceDistance | Distance Between Linear Spaces |
| suffstat | Sufficient Statistic of Point Process Model |
| summary.dppm | Summarizing a Fitted Determinantal Point Process Model |
| summary.fii | Methods for Fitted Interactions |
| summary.kppm | Summarizing a Fitted Cox or Cluster Point Process Model |
| summary.objsurf | Methods for Objective Function Surfaces |
| summary.ppm | Summarizing a Fitted Point Process Model |
| summary.slrm | Methods for Spatial Logistic Regression Models |
| summary.ssf | Methods for Spatially Sampled Functions |
| terms.dppm | Methods for Determinantal Point Process Models |
| terms.kppm | Methods for Cluster Point Process Models |
| terms.mppm | Log Likelihood and AIC for Multiple Point Process Model |
| terms.ppm | Model Formulae for Gibbs Point Process Models |
| terms.slrm | Methods for Spatial Logistic Regression Models |
| thomas.estK | Fit the Thomas Point Process by Minimum Contrast |
| thomas.estpcf | Fit the Thomas Point Process by Minimum Contrast |
| thresholdCI | Confidence Interval for Threshold of Numerical Predictor |
| thresholdSelect | Select Threshold to Convert Numerical Predictor to Binary Predictor |
| totalVariation | Positive and Negative Parts, and Variation, of a Measure |
| transect.im | Pixel Values Along a Transect |
| triplet.family | Triplet Interaction Family |
| Triplets | The Triplet Point Process Model |
| Tstat | Third order summary statistic |
| unitname.dppm | Name for Unit of Length |
| unitname.kppm | Name for Unit of Length |
| unitname.minconfit | Name for Unit of Length |
| unitname.ppm | Name for Unit of Length |
| unitname.slrm | Name for Unit of Length |
| unitname<-.dppm | Name for Unit of Length |
| unitname<-.kppm | Name for Unit of Length |
| unitname<-.minconfit | Name for Unit of Length |
| unitname<-.ppm | Name for Unit of Length |
| unitname<-.slrm | Name for Unit of Length |
| unmark.ssf | Methods for Spatially Sampled Functions |
| unstack.msr | Separate a Vector Measure into its Scalar Components |
| update.detpointprocfamily | Set Parameter Values in a Determinantal Point Process Model |
| update.interact | Update an Interpoint Interaction |
| update.kppm | Update a Fitted Cluster Point Process Model |
| update.ppm | Update a Fitted Point Process Model |
| update.slrm | Methods for Spatial Logistic Regression Models |
| valid | Check Whether Point Process Model is Valid |
| valid.detpointprocfamily | Check Validity of a Determinantal Point Process Model |
| valid.ppm | Check Whether Point Process Model is Valid |
| valid.slrm | Check Whether Spatial Logistic Regression Model is Valid |
| varblock | Estimate Variance of Summary Statistic by Subdivision |
| varcount | Predicted Variance of the Number of Points |
| vargamma.estK | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel |
| vargamma.estpcf | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel |
| vcov.kppm | Variance-Covariance Matrix for a Fitted Cluster Point Process Model |
| vcov.mppm | Calculate Variance-Covariance Matrix for Fitted Multiple Point Process Model |
| vcov.ppm | Variance-Covariance Matrix for a Fitted Point Process Model |
| vcov.slrm | Variance-Covariance Matrix for a Fitted Spatial Logistic Regression |
| Vmark | Diagnostics for random marking |
| Window.dppm | Extract Window of Spatial Object |
| Window.influence.ppm | Methods for Influence Objects |
| Window.kppm | Extract Window of Spatial Object |
| Window.leverage.ppm | Methods for Leverage Objects |
| Window.msr | Extract Window of Spatial Object |
| Window.ppm | Extract Window of Spatial Object |
| Window.quadrattest | Extract Window of Spatial Object |
| Window.slrm | Extract Window of Spatial Object |
| with.fv | Evaluate an Expression in a Function Table |
| with.msr | Evaluate Expression Involving Components of a Measure |
| with.ssf | Evaluate Expression in a Spatially Sampled Function |
| zclustermodel | Cluster Point Process Model |
| zgibbsmodel | Gibbs Model |
| $<-.fv | Extract or Replace Subset of Function Values |
| [.fasp | Extract Subset of Function Array |
| [.fv | Extract or Replace Subset of Function Values |
| [.influence.ppm | Extract Subset of Influence Object |
| [.leverage.ppm | Extract Subset of Leverage Object |
| [.msr | Extract Subset of Signed or Vector Measure |
| [.ssf | Subset of spatially sampled function |
| [<-.fv | Extract or Replace Subset of Function Values |