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spatstat.core (version 1.65-5)

Core Functionality of the 'spatstat' Package

Description

This is a subset of the original 'spatstat' package, containing all of the user-level code from 'spatstat', except for the code for linear networks.

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Version

Install

install.packages('spatstat.core')

Monthly Downloads

158

Version

1.65-5

License

GPL (>= 2)

Maintainer

Adrian Baddeley

Last Published

February 1st, 2021

Functions in spatstat.core (1.65-5)

DiggleGatesStibbard

Diggle-Gates-Stibbard Point Process Model

The Area Interaction Point Process Model

Extract.leverage.ppm

Extract Subset of Leverage Object

Hybrid Geyer Point Process Model

The Fiksel Interaction

Extract Subset of Signed or Vector Measure

Inhomogeneous Empty Space Function

Estimate the Empty Space Function or its Hazard Rate

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

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

Inhomogeneous Marked F-Function

Inhomogeneous Multitype K Dot Function

Cumulative Distribution Function From Kernel Density Estimate

K Function or Pair Correlation Function of Gibbs Point Process model

K Function or Pair Correlation Function of Cluster Model or Cox model

Extract or Replace Subset of Function Values

Extract.influence.ppm

Extract Subset of Influence Object

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

Ord's Interaction model

The Connected Component Process Model

Marked Nearest Neighbour Distance Function

Residual G Function

Smooth a Signed or Vector-Valued Measure

Spatial smoothing of observations at irregular points

Empty Space Function of a Three-Dimensional Point Pattern

Subset of spatially sampled function

The Hard Core Point Process Model

Inhomogeneous Marked G-Function

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

Model Compensator of Nearest Neighbour Function

Nearest Neighbour Distance Function G

The Hierarchical Strauss Point Process Model

Multitype J Function (i-to-j)

Geyer's Saturation Point Process Model

Convert Data To Class owin

adaptive.density

Adaptive Estimate of Intensity of Point Pattern

HierStraussHard

The Hierarchical Strauss Hard Core Point Process Model

Extract Fitted Point Process Model

Multitype J Function (i-to-any)

Added Variable Plot for Point Process Model

Estimate the J-function

Model Compensator of K Function

Multitype K Function (Cross-type)

Inhomogeneous J-function

Spherical Contact Distribution Function

Inhomogeneous K-function

K-function using FFT

K-function or Pair Correlation Function of a Determinantal Point Process Model

K Function or Pair Correlation Function of a Point Process Model

Global Envelopes for Balanced Independent Two-Stage Test

The Hierarchical Hard Core Point Process Model

The Lennard-Jones Potential

Balanced Independent Two-Stage Monte Carlo Test

Inhomogeneous Cross K Function

Inhomogeneous Marked K-Function

Marked K-Function

Generic Pairwise Interaction model

Multitype K Function (i-to-any)

Sector K-function

The Piecewise Constant Pairwise Interaction Point Process Model

Mark-Weighted K Function

Reduced Second Moment Measure

Piecewise Constant Saturated Pairwise Interaction Point Process Model

Saturated Pairwise Interaction model

Inhomogeneous L-function

Spatial Distribution Test for Point Pattern or Point Process Model

Fit the Neyman-Scott cluster process with Cauchy kernel

Lambert's W Function

Penttinen Interaction

The Multitype Hard Core Point Process Model

Foxall's Distance Functions

Inhomogeneous Nearest Neighbour Function

Smooth a Spatially Sampled Function

Smooth Interpolation of Marks as a Spatial Function

Poisson Point Process Model

Residual K Function

Hybrid Interaction Point Process Model

Estimate the I-function

Arithmetic Operations on Measures

Inhomogeneous Cross Type L Function

Multitype L-function (cross-type)

closepaircounts

Count Close Pairs of Points

Locally Scaled K-function

ANOVA for Fitted Point Process Models

MultiStraussHard

The Multitype/Hard Core Strauss Point Process Model

The Triplet Point Process Model

The Multitype Strauss Point Process Model

Apply Smoothing to Function Values

ANOVA for Fitted Point Process Models for Replicated Patterns

Spatial smoothing of data

Convert Function Value Table to Function

as.function.leverage.ppm

Convert Leverage Object to Function of Coordinates

The Strauss / Hard Core Point Process Model

Analysis of Deviance for Spatial Logistic Regression Models

K-function of a Three-Dimensional Point Pattern

Marked J Function

Generic Ord Interaction model

The Strauss Point Process Model

The Soft Core Point Process Model

Calculate four standard summary functions of a point pattern.

Field of clusters

Test Whether Function Objects Are Compatible

Generic Calculation of K Function and Pair Correlation Function

Area Under ROC Curve

Extract Interaction Structure

Calculate Summary Statistic for All Types in a Multitype Point Pattern

Convert Measure To Layered Object

Scott's Rule for Bandwidth Selection for Kernel Density

Cross Validated Bandwidth Selection for Spatial Smoothing

as.data.frame.envelope

Coerce Envelope to Data Frame

Third order summary statistic

Multitype L-function (i-to-any)

Bias Correction for Fitted Model

Extract Window of Spatial Object

Inhomogeneous Multitype L Dot Function

Abramson's Adaptive Bandwidths

Diggle-Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests

Significance Trace of Cressie-Loosmore-Ford or Maximum Absolute Deviation Test

Apply Gaussian Blur to a Pixel Image

Cross Validated Bandwidth Selection for Pair Correlation Function

Bandwidth Selection Based on Window Geometry

Cross Validated Bandwidth Selection for Kernel Density

as.function.rhohat

Convert Function Table to Function

Cronie and van Lieshout's Criterion for Bandwidth Selection for Kernel Density

Stoyan's Rule of Thumb for Bandwidth Selection

Convert Data To Class fv

Fit the Neyman-Scott cluster process with Cauchy kernel

Compute or Extract Effective Range of Cluster Kernel

Allard-Fraley Estimator of Cluster Feature

Combine Function Value Tables

Berman's Tests for Point Process Model

Extract Original Data from a Fitted Point Process Model

Progress Plot of Test of Spatial Pattern

Global Envelopes for Dao-Genton Test

Extract Cluster Offspring Kernel

Fit Cluster or Cox Point Process Model via Minimum Contrast

Clark and Evans Aggregation Index

Likelihood Cross Validation Bandwidth Selection for Kernel Density

compatible.fasp

Test Whether Function Arrays Are Compatible

Residual Diagnostics for Multiple Fitted Models

Progress Plot of Dao-Genton Test of Spatial Pattern

density.splitppp

Kernel Smoothed Intensity of Split Point Pattern

clarkevans.test

Clark and Evans Test

densityAdaptiveKernel

Adaptive Kernel Estimate of Intensity of Point Pattern

Cross Validated Bandwidth Selection for Relative Risk Estimation

Coefficients of Fitted Point Process Model

Coefficients of Point Process Model Fitted to Multiple Point Patterns

Diagnostic Plots for Fitted Point Process Model

dim.detpointprocfamily

Dimension of Determinantal Point Process Model

Bessel Type Determinantal Point Process Model

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

Exponential Energy Marks

Extract the Domain of any Spatial Object

Extract Spectral Density from Determinantal Point Process Model Object

Density Estimation for Circular Data

Spatial Distribution Test for Multiple Point Process Model

Kernel distributions and random generation

Default Expansion Rule for Simulation of Model

dppspecdenrange

Range of Spectral Density of a Determinantal Point Process Model

Kernel Smoothed Intensity of Point Pattern

Kernel Smoothing of Line Segment Pattern

Calculate Derivative of Function Values

Extract or Change the Plot Formula for a Function Value Table

Influence Measure for Spatial Point Process Model

Model Formulae for Gibbs Point Process Models

Mixed Poisson Distribution

Simulation Envelopes of Summary Function

Test Whether A Point Process Model is Marked

inforder.family

Infinite Order Interaction Family

is.multitype.ppm

Test Whether A Point Process Model is Multitype

Coefficients of Fitted Spatial Logistic Regression Model

Fit Determinantal Point Process Model

default.rmhcontrol

Set Default Control Parameters for Metropolis-Hastings Algorithm.

detpointprocfamilyfun

Construct a New Determinantal Point Process Model Family Function

Collapse Several Function Tables into One

Power Exponential Spectral Determinantal Point Process Model

Whittle-Matern Determinantal Point Process Model

Extract Kernel from Determinantal Point Process Model Object

Internal function calculating eig and index

Intensity Estimate of Point Pattern Using Voronoi-Dirichlet Tessellation

Kernel Estimate of Intensity as a Spatial Function

Leverage Measure for Spatial Point Process Model

Fit a Log-Gaussian Cox Point Process by Minimum Contrast

envelope.envelope

Recompute Envelopes

Significance Trace of Dao-Genton Test

Dao-Genton Adjusted Goodness-Of-Fit Test

Gaussian Determinantal Point Process Model

Generalized Cauchy Determinantal Point Process Model

dppapproxkernel

Approximate Determinantal Point Process Kernel

Extract Fixed Effects from Point Process Model

Parameter Influence Measure

Fitted Probabilities for Spatial Logistic Regression

Abbreviations for Groups of Columns in Function Value Table

Approximate Pair Correlation Function of Determinantal Point Process Model

Intensity of Determinantal Point Process Model

Integral of a Measure

Test Whether An Object Is A Fitted Point Process Model

Function Value Table

Ripley's Isotropic Edge Correction

Convert Data to Numeric Values by Constructing Dummy Variables

Case Deletion Effect Measure of Fitted Model

Parameter Bound for a Determinantal Point Process Model

Distribution Function of Interpoint Distance

Estimate Dimension of Central Subspace

Force Model to be Valid

Force Point Process Model to be Valid

Translation Edge Correction

Recognise Stationary and Poisson Point Process Models

Extract Dummy Points Used to Fit a Point Process Model

Fitted Conditional Intensity for Multiple Point Process Model

Fitted Conditional Intensity for Point Process Model

Evaluate Expression Involving Function Arrays

Evaluate Expression Involving Functions

Fry Plot of Point Pattern

Method of Minimum Contrast

Intensity of Fitted Point Process Model

markmarkscatter

Mark-Mark Scatter Plot

Tabulate Marks in Neighbourhood of Every Point in a Point Pattern

Extract the Interaction from a Fitted Point Process Model

Function Arrays for Spatial Patterns

Simulation Envelopes of Summary Function for 3D Point Pattern

Array of Simulation Envelopes of Summary Function

Hybrid Interaction Family

methods.leverage.ppm

Methods for Leverage Objects

Methods for Cluster Point Process Models

Local pair correlation function

Log Likelihood and AIC for Fitted Determinantal Point Process Model

Hopkins-Skellam Test

Inverse-distance weighted smoothing of observations at irregular points

Create a Function Value Table

Make Function Tables Compatible

Make Measures Compatible

Improve Intensity Estimate of Fitted Cluster Point Process Model

exactMPLEstrauss

Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process

Kaplan-Meier and Reduced Sample Estimator using Histograms

Apply Expansion Rule

hierpair.family

Hierarchical Pairwise Interaction Process Family

Integral of Squared Kernel

Fit Point Process Model Involving Irregular Trend Parameters

Methods for Intensity Functions of Spatial Covariate

Signed or Vector-Valued Measure

Methods for Spatial Logistic Regression Models

Increments of a Function

Neighbourhood density function

Loglikelihood of Spatial Logistic Regression

Log Likelihood and AIC for Point Process Model

Fit a Log-Gaussian Cox Point Process by Minimum Contrast

Gauss-Hermite Quadrature Approximation to Expectation for Normal Distribution

measureVariation

Positive and Negative Parts, and Variation, of a Measure

Interaction Structure Family Objects

Methods for Determinantal Point Process Models

Basis for Harmonic Functions

Plot a plotppm Object Created by plot.ppm

Mark Correlation Function

Mark Cross-Correlation Function

matclust.estpcf

Fit the Matern Cluster Point Process by Minimum Contrast Using Pair Correlation

measureContinuous

Discrete and Continuous Components of a Measure

Laslett's Transform

Perfect Simulation of the Penttinen Process

Compute Images of Constructed Covariates

Local Multitype K Function (Cross-Type)

localKcross.inhom

Inhomogeneous Multitype K Function

Recognise Fitted Determinantal Point Process Models

Lurking Variable Plot

Nearest-Neighbour Correlation Indices of Marked Point Pattern

Kaplan-Meier Estimator using Histogram Data

model.matrix.mppm

Extract Design Matrix of Point Process Model for Several Point Patterns

Inhomogeneous Multitype Pair Correlation Function (Cross-Type)

Fit Point Process Model to Several Point Patterns

Extract Model Parameters in Understandable Form

Test Whether Object is a Hybrid

Scale factor for density kernel

Plot a Function Array

Partial Residuals for Point Process Model

Estimate Intensity of Point Pattern Using Nearest Neighbour Distances

Fit the Matern Cluster Point Process by Minimum Contrast

Fit Cluster or Cox Point Process Model

Lurking Variable Plot for Multiple Point Patterns

Morisita Index Plot

Log Likelihood and AIC for Fitted Cox or Cluster Point Process Model

Moment of Smoothing Kernel

Alternating Gibbs Sampler for Area-Interaction Process

Dummy Function Returns Number of Points

Objective Function Surface

Bootstrap Confidence Bands for Summary Function

pairwise.family

Pairwise Interaction Process Family

Log Likelihood and AIC for Multiple Point Process Model

Panel Plots using Colour Image or Contour Lines

Inhomogeneous Neighbourhood Density Function

Scatterplot Matrix for Pixel Images

Local Multitype K Function (Dot-Type)

Plot a Spatial Distribution Test

Mark Connection Function

Saturated Pairwise Interaction Point Process Family

Multitype pair correlation function (i-to-any)

Rotational Average of a Pixel Image

Polynomial in One or Two Variables

Plot a Simulation Envelope

plot.leverage.ppm

Plot Leverage Function

Pseudoscore Diagnostic For Fitted Model against Area-Interaction Alternative

Alternating Gibbs Sampler for Multitype Point Processes

Fit Point Process Model to Point Pattern Data

Plot Function Values

plot.influence.ppm

Plot Influence Measure

Spatial Scan Test

Reduced Sample Estimator using Histogram Data

Nearest Neighbour Orientation Distribution

Methods for Fitted Interactions

Pool Data from a List of Objects

Simulate Neyman-Scott Process

methods.influence.ppm

Methods for Influence Objects

Pair Correlation Function of a Three-Dimensional Point Pattern

Pair Correlation Function of Point Pattern

model.matrix.ppm

Extract Design Matrix from Point Process Model

model.matrix.slrm

Extract Design Matrix from Spatial Logistic Regression Model

Methods for Spatially Sampled Functions

methods.zclustermodel

Methods for Cluster Models

Leverage and Influence Measures for Spatial Point Process Model

methods.objsurf

Methods for Objective Function Surfaces

methods.rho2hat

Methods for Intensity Functions of Two Spatial Covariates

Plot a Fitted Spatial Logistic Regression

Plot Result of Scan Test

Prune a Recursively Partitioned Point Process Model

quantile.density

Quantiles of a Density Estimate

Multitype pair correlation function (cross-type)

plot.studpermutest

Plot a Studentised Permutation Test

Pair Correlation Function obtained from array of K functions

Identify Covariates Involved in each Model Term

Simulate Matern Cluster Process

Print a Fitted Point Process Model

Fit Models by Profile Maximum Pseudolikelihood or AIC

Parametric Estimate of Spatially-Varying Relative Risk

quadratresample

Resample a Point Pattern by Resampling Quadrats

Class of Fitted Point Process Models

pool.quadrattest

Pool Several Quadrat Tests

Pair Correlation Function obtained from K Function

Generate N Random Points

Pool Data from Several Ratio Objects

plot a Fitted Multiple Point Process Model

Inhomogeneous Multitype Pair Correlation Function (Type-i-To-Any-Type)

Calculate Pseudo-R-Squared for Point Process Model

Contact Distribution Function using Rectangular Structuring Element

Residuals for Fitted Determinantal Point Process Model

Generate Poisson Random Line Process

Simulate Matern Model I

Repulsiveness Index of a Determinantal Point Process Model

Pair Correlation Function

Alternating Gibbs Sampler for Multitype Hard Core Process

Richardson Extrapolation

Random Pixel Noise

quadrat.test.splitppp

Dispersion Test of CSR for Split Point Pattern Based on Quadrat Counts

Simulate Matern Model II

Point Pair Orientation Distribution

Define Point Process Model for Metropolis-Hastings Simulation.

Nearest Neighbour Clutter Removal

Plot a fitted determinantal point process

Simulation of Determinantal Point Process Model

Plot a Signed or Vector-Valued Measure

Pool Data from Several Envelopes

plot a Fitted Point Process Model

Simulate Gauss-Poisson Process

Extract Random Effects from Point Process Model

Sibling Probability of Cluster Point Process

Marked pair correlation function

Predicted or Fitted Values from Spatial Logistic Regression

plot.bermantest

Plot Result of Berman Test

model.frame.ppm

Extract the Variables in a Point Process Model

plot.quadrattest

Display the result of a quadrat counting test.

Plot a Recursively Partitioned Point Process Model

Plot Profile Likelihood

Inhomogeneous Pair Correlation Function

Likelihood Ratio Test Statistic for Scan Test

Plot a Spatially Sampled Function

Ord Interaction Process Family

Pseudoscore Diagnostic For Fitted Model against Saturation Alternative

Theoretical Distribution of Nearest Neighbour Distance

Interpret Fitted Model for Metropolis-Hastings Simulation.

Prediction from a Fitted Determinantal Point Process Model

Residuals for Fitted Point Process Model

Perfect Simulation of the Diggle-Gratton Process

Nonparametric Estimate of Spatially-Varying Relative Risk

Simulate Log-Gaussian Cox Process

Fit Point Process Model to Data

Sufficient Dimension Reduction

Receiver Operating Characteristic

quadrat.test.mppm

Chi-Squared Test for Multiple Point Process Model Based on Quadrat Counts

Estimate of Spatially-Varying Relative Risk

Perfect Simulation of the Strauss-Hardcore Process

Prediction from a Fitted Cluster Point Process Model

Perfect Simulation of the Strauss Process

Dispersion Test for Spatial Point Pattern Based on Quadrat Counts

reload.or.compute

Compute Unless Previously Saved

Determine Initial State for Metropolis-Hastings Simulation.

Specify Simulation Window or Expansion Rule

Simulate point patterns using the Metropolis-Hastings algorithm.

Perfect Simulation of the Hardcore Process

Generate Random Numbers of Points for Cell Process

Simulate Baddeley-Silverman Cell Process

Extract List of Individual Point Process Models

Perfect Simulation of the Diggle-Gates-Stibbard Process

Make Predictions From a Recursively Partitioned Point Process Model

Mosaic Random Set

Interaction Distance of a Point Process

Pseudoscore Diagnostic For Fitted Model against General Alternative

Summarizing a Fitted Cox or Cluster Point Process Model

Smoothed Relative Density of Pairs of Covariate Values

Random Re-Labelling of Point Pattern

Simulate Thomas Process

Nonparametric Estimate of Intensity as Function of a Covariate

segregation.test

Test of Spatial Segregation of Types

Spatial Cumulative Distribution Function

Generate Poisson Point Pattern in Three Dimensions

Plot a fitted cluster point process

Plot Laslett Transform

Simulate Neyman-Scott Point Process with Cauchy cluster kernel

Residuals for Fitted Cox or Cluster Point Process Model

Mosaic Random Field

Residuals for Point Process Model Fitted to Multiple Point Patterns

rmhmodel.default

Build Point Process Model for Metropolis-Hastings Simulation.

Simulation of a Determinantal Point Process

Update a Fitted Point Process Model

Prediction for Fitted Multiple Point Process Model

Simulate a Fitted Gibbs Point Process Model

Pool Data from Several Function Arrays

Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel

subspaceDistance

Distance Between Linear Spaces

Simulate from a Fitted Point Process Model

Define Point Process Model for Metropolis-Hastings Simulation.

Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel

Random Thinning of Clumps

Generate Poisson Point Pattern in Any Dimensions

Random Thinning

Recursively Partitioned Point Process Model

Set Control Parameters for Metropolis-Hastings Algorithm.

Spatial Logistic Regression

Generate Multitype Poisson Point Pattern

Sufficient Statistic of Point Process Model

Test Expansion Rule

Randomly Shift a Point Pattern

Simulate Stratified Random Point Pattern

runifpointOnLines

Generate N Uniform Random Points On Line Segments

Simulate Point Process Models using the Metropolis-Hastings Algorithm.

Data Sharpening of Point Pattern

Simulate a Fitted Spatial Logistic Regression Model

Simulated Annealing or Simulated Tempering for Gibbs Point Processes

Pool Several Functions

Generate N Uniform Random Points in Three Dimensions

Q-Q Plot of Residuals from Fitted Point Process Model

Prediction from a Fitted Point Process Model

spatstat.core-package

The spatstat.core Package

Generate N Random Multitype Points

Calculate Variance-Covariance Matrix for Fitted Multiple Point Process Model

Extract Quadrature Scheme Used to Fit a Point Process Model

Pixel Values Along a Transect

Compute Integral of Function Against Cumulative Distribution

Generate Poisson Point Pattern on Line Segments

Generate N Uniform Random Points in Any Dimensions

valid.detpointprocfamily

Check Validity of a Determinantal Point Process Model

Spatially Sampled Function

Range of Interaction for a Determinantal Point Process Model

rPoissonCluster

Simulate Poisson Cluster Process

Simulate Simple Sequential Inhibition

Update a Fitted Cluster Point Process Model

Divide a Measure into Parts

Summarizing a Fitted Point Process Model

Range of Interaction for a Cox or Cluster Point Process Model

Generate Poisson Point Pattern

Range of Function Values

Stienen Diagram

Summarizing a Fitted Determinantal Point Process Model

Cluster Point Process Model

Check Whether Point Process Model is Valid

Triplet Interaction Family

Poisson Line Tessellation

vargamma.estpcf

Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel

Variance-Covariance Matrix for a Fitted Spatial Logistic Regression

spatstat.core-internal

Internal spatstat.core functions

Predicted Variance of the Number of Points

Estimate Variance of Summary Statistic by Subdivision

Variance-Covariance Matrix for a Fitted Cluster Point Process Model

Evaluate Expression in a Spatially Sampled Function

Fit the Thomas Point Process by Minimum Contrast

Randomly Shift a Line Segment Pattern

Evaluate an Expression in a Function Table

Fit the Thomas Point Process by Minimum Contrast

Separate a Vector Measure into its Scalar Components

Compute Predictors from Sufficient Dimension Reduction

Generate N Uniform Random Points

rshift.splitppp

Randomly Shift a List of Point Patterns

Generate N Uniform Random Points in a Disc

Variance-Covariance Matrix for a Fitted Point Process Model

update.rmhcontrol

Update Control Parameters of Metropolis-Hastings Algorithm

Studentised Permutation Test

spatstat.core-deprecated

Deprecated spatstat.core functions

Check Whether Point Process Model is Valid

Simulate a Fitted Cluster Point Process Model

Simulate a Point Process Model Fitted to Several Point Patterns

Name for Unit of Length

update.detpointprocfamily

Set Parameter Values in a Determinantal Point Process Model

update.interact

Update an Interpoint Interaction

Evaluate Expression Involving Components of a Measure

Diagnostics for random marking

Extract Subset of Function Array

Diggle-Gratton model