# Core Functionality of the 'spatstat' Family

## Documentation for package ‘spatstat.core’ version 2.3-2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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

## -- A --

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.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.rmhmodel | 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

## -- B --

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

## -- C --

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 | Field of clusters
clusterfield.character | Field of clusters
clusterfield.function | Field of clusters
clusterfield.kppm | Field of clusters
clusterfit | Fit Cluster or Cox Point Process Model via Minimum Contrast
clusterkernel | Extract Cluster Offspring Kernel
clusterkernel.character | Extract Cluster Offspring Kernel
clusterkernel.kppm | Extract Cluster Offspring Kernel
clusterradius | Compute or Extract Effective Range of Cluster Kernel
clusterradius.character | Compute or Extract Effective Range of Cluster 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

## -- D --

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
default.expand | Default Expansion Rule for Simulation of Model
default.rmhcontrol | Set Default Control Parameters for Metropolis-Hastings Algorithm.
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
dknn | Theoretical Distribution of Nearest Neighbour Distance
dmixpois | Mixed Poisson Distribution
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.rmhmodel | 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

## -- E --

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
expand.owin | Apply Expansion Rule
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

## -- F --

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

## -- G --

G3est | Nearest Neighbour Distance Distribution Function of a Three-Dimensional Point Pattern
gauss.hermite | Gauss-Hermite Quadrature Approximation to Expectation for Normal Distribution
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

## -- H --

Hardcore | The Hard Core 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

## -- I --

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
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 | Recognise Stationary and Poisson Point Process Models
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.rmhmodel | Recognise Stationary and Poisson Point Process Models
is.poisson.slrm | Recognise Stationary and Poisson Point Process 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 | Recognise Stationary and Poisson Point Process Models
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.rmhmodel | Recognise Stationary and Poisson Point Process Models
is.stationary.slrm | Recognise Stationary and Poisson Point Process Models
isf.object | Interaction Structure Family Objects

## -- J --

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

## -- K --

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

## -- L --

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
lurking.slrm | Lurking Variable Plot for Spatial Logistic Regression

## -- M --

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
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

## -- N --

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

## -- O --

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

## -- P --

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
pknn | Theoretical Distribution of Nearest Neighbour Distance
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
pmixpois | Mixed Poisson Distribution
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
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

## -- Q --

qkernel | Kernel distributions and random generation
qknn | Theoretical Distribution of Nearest Neighbour Distance
qmixpois | Mixed Poisson Distribution
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
quadratresample | Resample a Point Pattern by Resampling Quadrats
quantile.density | Quantiles of a Density Estimate

## -- R --

rags | Alternating Gibbs Sampler for Multitype Point Processes
ragsAreaInter | Alternating Gibbs Sampler for Area-Interaction Process
ragsMultiHard | Alternating Gibbs Sampler for Multitype Hard Core Process
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
rCauchy | Simulate Neyman-Scott Point Process with Cauchy cluster kernel
rcell | Simulate Baddeley-Silverman Cell Process
rcellnumber | Generate Random Numbers of Points for Cell Process
rDGS | Perfect Simulation of the Diggle-Gates-Stibbard Process
rDiggleGratton | Perfect Simulation of the Diggle-Gratton Process
rdpp | Simulation of a Determinantal Point Process
reach | Interaction Distance of a 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
reach.interact | Interaction Distance of a Point Process
reach.kppm | Range of Interaction for a Cox or Cluster Point Process Model
reach.ppm | Interaction Distance of a Point Process
reach.rmhmodel | Interaction Distance of a Point Process
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
rGaussPoisson | Simulate Gauss-Poisson Process
rHardcore | Perfect Simulation of the Hardcore Process
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
rjitter.psp | Random Perturbation of Line Segment Pattern
rkernel | Kernel distributions and random generation
rknn | Theoretical Distribution of Nearest Neighbour Distance
rlabel | Random Re-Labelling of Point Pattern
rLGCP | Simulate Log-Gaussian Cox Process
rMatClust | Simulate Matern Cluster Process
rMaternI | Simulate Matern Model I
rMaternII | Simulate Matern Model II
rmax.Ripley | Ripley's Isotropic Edge Correction
rmax.Trans | Translation Edge Correction
rmh | Simulate point patterns using the Metropolis-Hastings algorithm.
rmh.default | Simulate Point Process Models using the Metropolis-Hastings Algorithm.
rmh.ppm | Simulate from a Fitted Point Process Model
rmhcontrol | Set Control Parameters for Metropolis-Hastings Algorithm.
rmhcontrol.default | Set Control Parameters for Metropolis-Hastings Algorithm.
rmhexpand | Specify Simulation Window or Expansion Rule
rmhmodel | Define Point Process Model for Metropolis-Hastings Simulation.
rmhmodel.default | Build Point Process Model for Metropolis-Hastings Simulation.
rmhmodel.list | Define Point Process Model for Metropolis-Hastings Simulation.
rmhmodel.ppm | Interpret Fitted Model for Metropolis-Hastings Simulation.
rmhstart | Determine Initial State for Metropolis-Hastings Simulation.
rmhstart.default | Determine Initial State for Metropolis-Hastings Simulation.
rmixpois | Mixed Poisson Distribution
rMosaicField | Mosaic Random Field
rMosaicSet | Mosaic Random Set
rmpoint | Generate N Random Multitype Points
rmpoispp | Generate Multitype Poisson Point Pattern
rNeymanScott | Simulate Neyman-Scott Process
rnoise | Random Pixel Noise
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
rPenttinen | Perfect Simulation of the Penttinen Process
rpoint | Generate N Random Points
rpoisline | Generate Poisson Random Line Process
rpoislinetess | Poisson Line Tessellation
rpoispp | Generate Poisson Point Pattern
rpoispp3 | Generate Poisson Point Pattern in Three Dimensions
rpoisppOnLines | Generate Poisson Point Pattern on Line Segments
rpoisppx | Generate Poisson Point Pattern in Any Dimensions
rPoissonCluster | Simulate Poisson Cluster Process
rppm | Recursively Partitioned Point Process Model
rPSNCP | Simulate Product Shot-noise Cox Process
rshift | Random Shift
rshift.ppp | Randomly Shift a Point Pattern
rshift.psp | Randomly Shift a Line Segment Pattern
rshift.splitppp | Randomly Shift a List of Point Patterns
rSSI | Simulate Simple Sequential Inhibition
rstrat | Simulate Stratified Random Point Pattern
rStrauss | Perfect Simulation of the Strauss Process
rStraussHard | Perfect Simulation of the Strauss-Hardcore Process
rtemper | Simulated Annealing or Simulated Tempering for Gibbs Point Processes
rthin | Random Thinning
rthinclumps | Random Thinning of Clumps
rThomas | Simulate Thomas Process
runifdisc | Generate N Uniform Random Points in a Disc
runifpoint | Generate N Uniform Random Points
runifpoint3 | Generate N Uniform Random Points in Three Dimensions
runifpointOnLines | Generate N Uniform Random Points On Line Segments
runifpointx | Generate N Uniform Random Points in Any Dimensions
rVarGamma | Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel

## -- S --

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

## -- T --

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

## -- U --

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.rmhcontrol | Update Control Parameters of Metropolis-Hastings Algorithm
update.slrm | Methods for Spatial Logistic Regression Models

## -- V --

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

## -- W --

will.expand | Test Expansion Rule
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.rmhmodel | 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

## -- Z --

zclustermodel | Cluster Point Process Model

## -- misc --

$<-.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