The Area Interaction Point Process Model
Residual G Function
The Hard Core Point Process Model
Hybrid Geyer Point Process Model
Cumulative Distribution Function From Kernel Density Estimate
The Hierarchical Hard Core Point Process Model
Spherical Contact Distribution Function
The Connected Component Process Model
Inhomogeneous Marked F-Function
Inhomogeneous Empty Space Function
Foxall's Distance Functions
Inhomogeneous Nearest Neighbour Function
Multitype J Function (i-to-j)
Multitype J Function (i-to-any)
Inhomogeneous Cross K Function
Model Compensator of K Function
Multitype K Function (Cross-type)
Marked K-Function
Inhomogeneous Marked K-Function
Multitype K Function (i-to-any)
Diagnostics for random marking
K Function or Pair Correlation Function of Gibbs Point Process model
K Function or Pair Correlation Function of Cluster Model or Cox model
The Multitype Hard Core Point Process Model
Arithmetic Operations on Measures
Generic Ord Interaction model
Inhomogeneous L-function
Inhomogeneous Multitype L Dot Function
Multitype L-function (i-to-any)
Penttinen Interaction
Spatial smoothing of data
Smooth a Spatially Sampled Function
Extract Subset of Function Array
Apply Smoothing to Function Values
Poisson Point Process Model
Extract Subset of Leverage Object
Extract Subset of Signed or Vector Measure
Smooth Interpolation of Marks as a Spatial Function
Multitype Nearest Neighbour Distance Function (i-to-j)
Adaptive Estimate of Intensity of Point Pattern
Added Variable Plot for Point Process Model
Analysis of Deviance for Spatial Logistic Regression Models
Coerce Envelope to Data Frame
Estimate the Empty Space Function or its Hazard Rate
The Hierarchical Strauss Point Process Model
The Fiksel Interaction
Apply Gaussian Blur to a Pixel Image
L-function
Residual K Function
Ord's Interaction model
The Hierarchical Strauss Hard Core Point Process Model
The Lennard-Jones Potential
Locally Scaled K-function
Multitype Nearest Neighbour Distance Function (i-to-any)
The Strauss / Hard Core Point Process Model
The Triplet Point Process Model
Scott's Rule for Bandwidth Selection for Kernel Density
Field of clusters
Count Close Pairs of Points
Cronie and van Lieshout's Criterion for Bandwidth Selection for Kernel Density
Cross Validated Bandwidth Selection for Spatial Smoothing
Transform a Function into its P-P or Q-Q Version
Smooth a Signed or Vector-Valued Measure
Diggle-Gratton model
Residual Diagnostics for Multiple Fitted Models
Diggle-Gates-Stibbard Point Process Model
Convert Function Value Table to Function
Spatial smoothing of observations at irregular points
Extract Interaction Structure
Convert Measure To Layered Object
Berman's Tests for Point Process Model
Nearest Neighbour Distance Distribution Function
of a Three-Dimensional Point Pattern
Marked Nearest Neighbour Distance Function
Test Whether Function Arrays Are Compatible
Model Compensator of Nearest Neighbour Function
Diggle-Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests
Inhomogeneous Marked G-Function
Default Expansion Rule for Simulation of Model
Estimate the J-function
Inhomogeneous J-function
Convert Leverage Object to Function of Coordinates
Bandwidth Selection for Diffusion Smoother by Cronie-van Lieshout Rule
Abramson's Adaptive Bandwidths
Marked J Function
K-function of a Three-Dimensional Point Pattern
K-function or Pair Correlation Function of a
Determinantal Point Process Model
K-function
Inhomogeneous Multitype K Dot Function
Generic Pairwise Interaction model
The Piecewise Constant Pairwise Interaction Point Process Model
Spatial Distribution Test for Multiple Point Process Model
K Function or Pair Correlation Function of a Point Process Model
The Multitype/Hard Core Strauss Point Process Model
Multitype L-function (cross-type)
The Soft Core Point Process Model
The Multitype Strauss Point Process Model
Inhomogeneous Cross Type L Function
Bandwidth Selection for Diffusion Smoother by Likelihood Cross-Validation
Cross Validated Bandwidth Selection for Relative Risk Estimation
Combine Function Value Tables
Significance Trace of Cressie-Loosmore-Ford or Maximum Absolute
Deviation Test
Coefficients of Point Process Model Fitted to Multiple Point Patterns
Progress Plot of Test of Spatial Pattern
Construct a New Determinantal Point Process Model Family Function
Density Estimation for Circular Data
Coefficients of Fitted Point Process Model
Piecewise Constant Saturated Pairwise Interaction Point Process Model
The Strauss Point Process Model
Saturated Pairwise Interaction model
Parameter Influence Measure
Significance Trace of Dao-Genton Test
Progress Plot of Dao-Genton Test of Spatial Pattern
Compute Fitted Effect of a Spatial Covariate in a Point Process Model
Force Model to be Valid
Model Formulae for Gibbs Point Process Models
Extract or Change the Plot Formula for a Function Value Table
Array of Simulation Envelopes of Summary Function
Simulation Envelopes of Summary Function for 3D Point Pattern
ANOVA for Fitted Point Process Models for Replicated Patterns
Convert Data To Class owin
Extract Fitted Point Process Model
ANOVA for Fitted Point Process Models
Global Envelopes for Balanced Independent Two-Stage Test
Third order summary statistic
Function Value Table
Abbreviations for Groups of Columns in Function Value Table
Mixed Poisson Distribution
Adaptive Kernel Estimate of Intensity of Point Pattern
Model selection criteria for the intensity function of a point process
Diffusion Estimate of Point Pattern Intensity
Stoyan's Rule of Thumb for Bandwidth Selection
Extract Window of Spatial Object
Inverse-distance weighted smoothing of observations at irregular points
Fit the Neyman-Scott cluster process with Cauchy kernel
Balanced Independent Two-Stage Monte Carlo Test
Extract the Domain of any Spatial Object
Extract Kernel from Determinantal Point Process Model Object
Recognise Stationary and Poisson Point Process Models
Interaction Structure Family Objects
Fit the Neyman-Scott cluster process with Cauchy kernel
Integral of Squared Kernel
Collapse Several Function Tables into One
Spatial Distribution Test for Point Pattern or Point Process Model
Moment of Smoothing Kernel
Coefficients of Fitted Spatial Logistic Regression Model
Convert Function Table to Function
Fit Determinantal Point Process Model
Loglikelihood of Spatial Logistic Regression
Saturated Pairwise Interaction Point Process Family
Methods for Intensity Functions of Two Spatial Covariates
Methods for Objective Function Surfaces
Log Likelihood and AIC for Point Process Model
Lurking Variable Plot
Estimate Intensity of Point Pattern Using Nearest Neighbour Distances
Bootstrap Confidence Bands for Summary Function
Test Whether Function Objects Are Compatible
Nearest Neighbour Orientation Distribution
Pairwise Interaction Process Family
Fit Cluster or Cox Point Process Model via Minimum Contrast
Bandwidth Selection Based on Window Geometry
Convert Data To Class fv
Kernel Smoothing of Line Segment Pattern
Cross Validated Bandwidth Selection for Kernel Density
Gauss-Hermite Quadrature Approximation to Expectation for Normal Distribution
Basis for Harmonic Functions
Infinite Order Interaction Family
Inhomogeneous Multitype Pair Correlation Function (Type-i-To-Any-Type)
Influence Measure for Spatial Point Process Model
Multitype pair correlation function (i-to-any)
Kernel Smoothed Intensity of Split Point Pattern
Extract Cluster Offspring Kernel
Covariance and Correlation between Images
Kernel Estimate of Intensity as a Spatial Function
Plot Influence Measure
Distribution Function of Interpoint Distance
Kernel distributions and random generation
Plot a fitted cluster point process
Calculate Derivative of Function Values
Dimension of Determinantal Point Process Model
Extract Original Data from a Fitted Point Process Model
Estimate Dimension of Central Subspace
Plot a Fitted Spatial Logistic Regression
Fit a Log-Gaussian Cox Point Process by Minimum Contrast
Neighbourhood density function
Generic Calculation of K Function and Pair Correlation Function
Subset of spatially sampled function
Extract Subset of Influence Object
Extract or Replace Subset of Function Values
Empty Space Function of a Three-Dimensional Point Pattern
Approximate Pair Correlation Function of Determinantal Point Process Model
Internal function calculating eig and index
Prediction from a Fitted Point Process Model
Plot a Spatially Sampled Function
Pool Data from Several Function Arrays
Make Predictions From a Recursively Partitioned Point Process Model
Pool Data from Several Envelopes
Bessel Type Determinantal Point Process Model
Case Deletion Effect Measure of Fitted Model
Generalized Cauchy Determinantal Point Process Model
Global Envelopes for Dao-Genton Test
Nearest Neighbour Distance Function G
Ripley's Isotropic Edge Correction
Evaluate Expression Involving Function Arrays
Fitted Conditional Intensity for Multiple Point Process Model
Evaluate Expression Involving Functions
Extract Dummy Points Used to Fit a Point Process Model
Geyer's Saturation Point Process Model
Local Multitype K Function (Dot-Type)
Hybrid Interaction Point Process Model
Gaussian Determinantal Point Process Model
Extract Spectral Density from Determinantal Point Process Model Object
Simulation Envelopes of Summary Function
Recompute Envelopes
Fry Plot of Point Pattern
Parameter Bound for a Determinantal Point Process Model
Make Function Tables Compatible
Inhomogeneous Neighbourhood Density Function
Whittle-Matern Determinantal Point Process Model
Fitted Conditional Intensity for Point Process Model
Range of Spectral Density of a Determinantal Point Process Model
K-function using FFT
Convert Data to Numeric Values by Constructing Dummy Variables
Force Point Process Model to be Valid
Estimate the I-function
Mark Connection Function
Lurking Variable Plot for Multiple Point Patterns
Fitted Probabilities for Spatial Logistic Regression
Inhomogeneous K-function
Force Spatial Logistic Regression Model to be Valid
Extract Fixed Effects from Point Process Model
Improve Intensity Estimate of Fitted Cluster Point Process Model
Method of Minimum Contrast
Methods for Cluster Point Process Models
Morisita Index Plot
Methods for Leverage Objects
Nearest Neighbour Clutter Removal
Q-Q Plot of Residuals from Fitted Point Process Model
Pseudoscore Diagnostic For Fitted Model against Saturation Alternative
Leverage and Influence Diagnostics for Spatial Logistic Regression
Log Likelihood and AIC for Fitted Cox or Cluster Point Process Model
Log Likelihood and AIC for Multiple Point Process Model
Test Whether Object is a Hybrid
Test Whether A Point Process Model is Marked
Fit a Log-Gaussian Cox Point Process by Minimum Contrast
Increments of a Function
Mark-Weighted K Function
Create a Function Value Table
Fit Point Process Model Involving Irregular Trend Parameters
Make Measures Compatible
Intensity of Fitted Spatial Logistic Regression Model
Leverage Measure for Spatial Point Process Model
Laslett's Transform
Intensity of Fitted Point Process Model
Heat Kernel for a Two-Dimensional Rectangle
Methods for Fitted Interactions
Nearest-Neighbour Correlation Indices of Marked Point Pattern
Recognise Fitted Determinantal Point Process Models
Fit Point Process Model to Several Point Patterns
Methods for Influence Objects
Hybrid Interaction Family
Extract Design Matrix of Point Process Model for Several Point Patterns
Compute Images of Constructed Covariates
Local pair correlation function
Partial Residuals for Point Process Model
Pair Correlation Function
Reduced Second Moment Measure
Pair Correlation Function of Point Pattern
Intensity of Determinantal Point Process Model
Test Whether A Point Process Model is Multitype
Integral of a Measure
Alternating Gibbs Sampler for Area-Interaction Process
Test Whether An Object Is A Fitted Point Process Model
Signed or Vector-Valued Measure
Point Pair Orientation Distribution
Plot a fitted determinantal point process
Alternating Gibbs Sampler for Multitype Point Processes
Kaplan-Meier and Reduced Sample Estimator using Histograms
Range of Interaction for a Determinantal Point Process Model
Pair Correlation Function of a Three-Dimensional Point Pattern
Sector K-function
Range of Interaction for a Cox or Cluster Point Process Model
Plot a Simulation Envelope
Plot Result of Scan Test
Fit Cluster or Cox Point Process Model
Plot a Recursively Partitioned Point Process Model
Lambert's W Function
Mark-Mark Scatter Plot
Tabulate Marks in Neighbourhood of Every Point in a Point Pattern
Identify Covariates Involved in each Model Term
Scatterplot Matrix for Pixel Images
Repulsiveness Index of a Determinantal Point Process Model
Log Likelihood and AIC for Fitted Determinantal Point Process Model
Residuals for Fitted Determinantal Point Process Model
Simulate Neyman-Scott Point Process with Cauchy cluster kernel
Perfect Simulation of the Diggle-Gates-Stibbard Process
Mosaic Random Field
Extract Random Effects from Point Process Model
Alternating Gibbs Sampler for Multitype Hard Core Process
Simulate Matern Model II
Local Multitype K Function (Cross-Type)
Pair Correlation Function obtained from array of K functions
Simulate Point Process Models using the Metropolis-Hastings Algorithm.
Area Under ROC Curve
Bias Correction for Fitted Model
Calculate four standard summary functions of a point pattern.
Cross Validated Bandwidth Selection for Pair Correlation Function
Calculate Summary Statistic for All Types in a Multitype Point Pattern
Clark and Evans Aggregation Index
Likelihood Cross Validation Bandwidth Selection for Kernel Density
Inhomogeneous Multitype K Function
Extract the Variables in a Point Process Model
Pair Correlation Function obtained from K Function
Mark Correlation Function
Mark Cross-Correlation Function
Plot Laslett Transform
Compute or Extract Effective Range of Cluster Kernel
Allard-Fraley Estimator of Cluster Feature
Clark and Evans Test
Sufficient Statistic of Point Process Model
Reduced Sample Estimator using Histogram Data
Generate N Uniform Random Points
Contact Distribution Function using Rectangular Structuring Element
Generate Poisson Random Line Process
Simulate from a Fitted Point Process Model
Specify Simulation Window or Expansion Rule
Set Control Parameters for Metropolis-Hastings Algorithm.
Extract Design Matrix from Point Process Model
Ord Interaction Process Family
Methods for Determinantal Point Process Models
Inhomogeneous Pair Correlation Function
Marked pair correlation function
Extract Design Matrix from Spatial Logistic Regression Model
Mean of a Function of Interpoint Distance
Positive and Negative Parts, and Variation, of a Measure
Methods for Spatially Sampled Functions
Plot a plotppm Object Created by plot.ppm
Poisson Line Tessellation
Dummy Function Returns Number of Points
Likelihood Ratio Test Statistic for Scan Test
Generate N Uniform Random Points On Line Segments
Methods for Cluster Models
Generate N Random Multitype Points
Determine Initial State for Metropolis-Hastings Simulation.
Objective Function Surface
Polynomial in One or Two Variables
Plot a Studentised Permutation Test
Plot Leverage Function
Set Default Control Parameters for Metropolis-Hastings Algorithm.
Plot Result of Berman Test
Spatial Logistic Regression
Simulate a Fitted Spatial Logistic Regression Model
Generate N Uniform Random Points in Any Dimensions
Multitype pair correlation function (cross-type)
Predicted or Fitted Values from Spatial Logistic Regression
Generate N Uniform Random Points in Three Dimensions
Inhomogeneous Multitype Pair Correlation Function (Cross-Type)
Plot a Spatial Distribution Test
Kernel Smoothed Intensity of Point Pattern
Diffusion Estimate of Point Pattern Intensity
Intensity Estimate of Point Pattern Using Voronoi-Dirichlet Tessellation
Print a Fitted Point Process Model
Extract List of Individual Point Process Models
Separate a Vector Measure into its Scalar Components
Distance Between Linear Spaces
Check Whether Spatial Logistic Regression Model is Valid
Name for Unit of Length
Pool Data
plot a Fitted Multiple Point Process Model
plot a Fitted Point Process Model
Power Exponential Spectral Determinantal Point Process Model
Diagnostic Plots for Fitted Point Process Model
Dao-Genton Adjusted Goodness-Of-Fit Test
Extract Quadrature Scheme Used to Fit a Point Process Model
Prediction from a Fitted Cluster Point Process Model
Pool Data from a List of Objects
Dispersion Test for Spatial Point Pattern Based on
Quadrat Counts
Simulate Thomas Process
Estimate Variance of Summary Statistic by Subdivision
Prediction for Fitted Multiple Point Process Model
Simulation of a Determinantal Point Process
Resample a Point Pattern by Resampling Quadrats
Spatial Scan Test
Pool Several Quadrat Tests
Pool Several Functions
Plot a Signed or Vector-Valued Measure
Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel
Leverage and Influence Measures for Spatial Point Process Model
Randomly Shift a List of Point Patterns
Mosaic Random Set
Quantiles of a Density Estimate
Deprecated spatstat.core functions
Evaluate Expression in a Spatially Sampled Function
Generate Poisson Point Pattern in Any Dimensions
Simulate Neyman-Scott Process
Variance-Covariance Matrix for a Fitted Cluster Point Process Model
Summarizing a Fitted Determinantal Point Process Model
Check Whether Point Process Model is Valid
Approximate Determinantal Point Process Kernel
Sibling Probability of Cluster Point Process
Simulate Gauss-Poisson Process
Calculate Pseudo-R-Squared for Point Process Model
Exponential Energy Marks
Translation Edge Correction
Extract the Values of the Response from a Fitted Model
Check Validity of a Determinantal Point Process Model
Generate N Uniform Random Points in a Disc
Define Point Process Model for Metropolis-Hastings Simulation.
Apply Expansion Rule
Function Arrays for Spatial Patterns
Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process
Extract the Interaction from a Fitted Point Process Model
Hierarchical Pairwise Interaction Process Family
Hopkins-Skellam Test
Prediction from a Fitted Determinantal Point Process Model
Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel
Scale factor for density kernel
Fit the Matern Cluster Point Process by Minimum Contrast
Mark Variogram
Kaplan-Meier Estimator using Histogram Data
Simulation of Determinantal Point Process Model
Residuals for Fitted Cox or Cluster Point Process Model
Simulate Poisson Cluster Process
Interaction Distance of a Point Process
Perfect Simulation of the Diggle-Gratton Process
Discrete and Continuous Components of a Measure
Fit the Matern Cluster Point Process by Minimum Contrast Using Pair Correlation
Residuals for Point Process Model Fitted to Multiple Point Patterns
Methods for Intensity Functions of Spatial Covariate
Generate Multitype Poisson Point Pattern
Simulate Stratified Random Point Pattern
Variance-Covariance Matrix for a Fitted Point Process Model
Interpret Fitted Model for Metropolis-Hastings Simulation.
Simulate Simple Sequential Inhibition
Generate Poisson Point Pattern in Three Dimensions
update.detpointprocfamily
Set Parameter Values in a Determinantal Point Process Model
Methods for Spatial Logistic Regression Models
Generate Poisson Point Pattern on Line Segments
Panel Plots using Colour Image or Contour Lines
Plot a Function Array
Extract Model Parameters in Understandable Form
Pseudoscore Diagnostic For Fitted Model against General Alternative
Calculate Variance-Covariance Matrix for Fitted Multiple Point
Process Model
Internal spatstat.core functions
Simulate a Fitted Cluster Point Process Model
Richardson Extrapolation
Plot Function Values
Spatially Sampled Function
Pseudoscore Diagnostic For Fitted Model against Area-Interaction Alternative
Display the result of a quadrat counting test.
Pool Data from Several Ratio Objects
Plot Profile Likelihood
Fit Point Process Model to Data
Class of Fitted Point Process Models
Compute Integral of Function Against Cumulative Distribution
Predicted Variance of the Number of Points
Fit Point Process Model to Point Pattern Data
Perfect Simulation of the Hardcore Process
Simulate Product Shot-noise Cox Process
Rotational Average of a Pixel Image
Update a Fitted Point Process Model
Random Pixel Noise
Random Re-Labelling of Point Pattern
Fit the Thomas Point Process by Minimum Contrast
Simulate point patterns using the Metropolis-Hastings algorithm.
Perfect Simulation of the Penttinen Process
Simulate Log-Gaussian Cox Process
Simulated Annealing or Simulated Tempering for Gibbs Point Processes
Fit the Thomas Point Process by Minimum Contrast
Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel
Generate Poisson Point Pattern
Fit Models by Profile Maximum Pseudolikelihood or AIC
Range of Function Values
Residuals for Fitted Point Process Model
Prune a Recursively Partitioned Point Process Model
Simulate Baddeley-Silverman Cell Process
Generate Random Numbers of Points for Cell Process
Chi-Squared Test for Multiple Point Process Model Based on
Quadrat Counts
Ratio object
Test of Spatial Segregation of Types
Build Point Process Model for Metropolis-Hastings Simulation.
Theoretical Distribution of Nearest Neighbour Distance
Residuals for Fitted Spatial Logistic Regression Model
Dispersion Test of CSR for Split Point Pattern Based on
Quadrat Counts
Perfect Simulation of the Strauss-Hardcore Process
Compute Unless Previously Saved
Estimate of Spatially-Varying Relative Risk
Perfect Simulation of the Strauss Process
Simulate Matern Model I
Parametric Estimate of Spatially-Varying Relative Risk
Nonparametric Estimate of Spatially-Varying Relative Risk
Define Point Process Model for Metropolis-Hastings Simulation.
Simulate Matern Cluster Process
Smoothed Relative Density of Pairs of Covariate Values
Random Thinning of Clumps
Update a Fitted Cluster Point Process Model
Evaluate an Expression in a Function Table
Estimate the Spatial Covariance Function of a Random Field
Random Perturbation of Line Segment Pattern
Random Thinning
Generate N Random Points
Simulate a Point Process Model Fitted to Several Point Patterns
Sufficient Dimension Reduction
Compute Predictors from Sufficient Dimension Reduction
Receiver Operating Characteristic
Rose Diagram
Random Shift
Recursively Partitioned Point Process Model
Nonparametric Estimate of Intensity as Function of a Covariate
Summarizing a Fitted Cox or Cluster Point Process Model
Data Sharpening of Point Pattern
Simulate a Fitted Gibbs Point Process Model
Spatial Cumulative Distribution Function
Update an Interpoint Interaction
Randomly Shift a Point Pattern
Cluster Point Process Model
Confidence Interval for Threshold of Numerical Predictor
Randomly Shift a Line Segment Pattern
Divide a Measure into Parts
Pixel Values Along a Transect
The spatstat.core Package
Check Whether Point Process Model is Valid
Stienen Diagram
Triplet Interaction Family
Update Control Parameters of Metropolis-Hastings Algorithm
Studentised Permutation Test
Variance-Covariance Matrix for a Fitted Spatial Logistic Regression
Test Expansion Rule
Summarizing a Fitted Point Process Model
Select Threshold to Convert Numerical Predictor to Binary Predictor
Evaluate Expression Involving Components of a Measure