All functions

oldcol

Columbus OH spatial analysis data set - old numbering

EBImoran.mc()

Permutation test for empirical Bayes index

EBest()

Global Empirical Bayes estimator

EBlocal()

Local Empirical Bayes estimator

LOSH()

Local spatial heteroscedasticity

LOSH.cs()

Chi-square based test for local spatial heteroscedasticity

LOSH.mc()

Bootstrapping-based test for local spatial heteroscedasticity

aggregate(<nb>)

Aggregate a spatial neighbours object

airdist()

Measure distance from plot

aple()

Approximate profile-likelihood estimator (APLE)

aple.mc()

Approximate profile-likelihood estimator (APLE) permutation test

aple.plot() localAple()

Approximate profile-likelihood estimator (APLE) scatterplot

autocov_dist()

Distance-weighted autocovariate

bhicv

Data set with 4 life condition indices of Belo Horizonte region

card()

Cardinalities for neighbours lists

cell2nb() mrc2vi() rookcell() queencell() vi2mrc()

Generate neighbours list for grid cells

choynowski()

Choynowski probability map values

columbus

Columbus OH spatial analysis data set

n.comp.nb()

Depth First Search on Neighbor Lists

diffnb()

Differences between neighbours lists

dnearneigh()

Neighbourhood contiguity by distance

droplinks()

Drop links in a neighbours list

edit(<nb>)

Interactive editing of neighbours lists

eire

Eire data sets

geary()

Compute Geary's C

geary.mc()

Permutation test for Geary's C statistic

geary.test()

Geary's C test for spatial autocorrelation

globalG.test()

Global G test for spatial autocorrelation

gabrielneigh() relativeneigh() soi.graph() graph2nb() plot(<Gabriel>) plot(<relative>)

Graph based spatial weights

grid2nb()

Construct neighbours for a GridTopology

include.self()

Include self in neighbours list

invIrM() invIrW()

Compute SAR generating operator

joincount.mc()

Permutation test for same colour join count statistics

joincount.multi() print(<jcmulti>)

BB, BW and Jtot join count statistic for k-coloured factors

joincount.test() print(<jclist>)

BB join count statistic for k-coloured factors

knearneigh()

K nearest neighbours for spatial weights

knn2nb()

Neighbours list from knn object

lag(<listw>)

Spatial lag of a numeric vector

lee()

Compute Lee's statistic

lee.mc()

Permutation test for Lee's L statistic

lee.test()

Lee's L test for spatial autocorrelation

listw2sn() sn2listw()

Spatial neighbour sparse representation

lm.LMtests() print(<LMtestlist>) summary(<LMtestlist>) print(<LMtestlist.summary>)

Lagrange Multiplier diagnostics for spatial dependence in linear models

lm.morantest() listw2U()

Moran's I test for residual spatial autocorrelation

lm.morantest.exact() print(<moranex>)

Exact global Moran's I test

lm.morantest.sad() print(<moransad>) summary(<moransad>) print(<summary.moransad>)

Saddlepoint approximation of global Moran's I test

localG()

G and Gstar local spatial statistics

localmoran()

Local Moran's I statistic

localmoran.exact() localmoran.exact.alt() print(<localmoranex>) as.data.frame(<localmoranex>)

Exact local Moran's Ii tests

localmoran.sad() print(<localmoransad>) summary(<localmoransad>) print(<summary.localmoransad>) listw2star()

Saddlepoint approximation of local Moran's Ii tests

mat2listw()

Convert a square spatial weights matrix to a weights list object

moran()

Compute Moran's I

moran.mc()

Permutation test for Moran's I statistic

moran.plot()

Moran scatterplot

moran.test()

Moran's I test for spatial autocorrelation

mstree()

Find the minimal spanning tree

nb2INLA()

Output spatial neighbours for INLA

nb2WB() listw2WB()

Output spatial weights for WinBUGS

nb2blocknb()

Block up neighbour list for location-less observations

nb2lines() listw2lines() df2sn()

Use vector files for import and export of weights

nb2listw()

Spatial weights for neighbours lists

nb2mat() listw2mat()

Spatial weights matrices for neighbours lists

nbcost() nbcosts()

Compute cost of edges

nbdists()

Spatial link distance measures

nblag() nblag_cumul()

Higher order neighbours lists

intersect.nb() union.nb() setdiff.nb() complement.nb()

Set operations on neighborhood objects

p.adjustSP()

Adjust local association measures' p-values

plot(<mst>)

Plot the Minimum Spanning Tree

plot(<nb>) plot(<listw>)

Plot a neighbours list

plot(<skater>)

Plot the object of skater class

poly2nb()

Construct neighbours list from polygon list

probmap()

Probability mapping for rates

prunecost()

Compute cost of prune each edge

prunemst()

Prune a Minimun Spanning Tree

read.gal() read.geoda()

Read a GAL lattice file into a neighbours list

read.gwt2nb() write.sn2gwt() read.dat2listw() write.sn2dat()

Read and write spatial neighbour files

Rotation()

Rotate a set of point by a certain angle

set.mcOption() get.mcOption() set.coresOption() get.coresOption() set.ClusterOption() get.ClusterOption()

Options for parallel support

set.spChkOption() get.spChkOption() chkIDs() spNamedVec() set.VerboseOption() get.VerboseOption() set.ZeroPolicyOption() get.ZeroPolicyOption() set.listw_is_CsparseMatrix_Option() get.listw_is_CsparseMatrix_Option()

Control checking of spatial object IDs

skater()

Spatial 'K'luster Analysis by Tree Edge Removal

sp.correlogram() plot(<spcor>) print(<spcor>)

Spatial correlogram

sp.mantel.mc() plot(<mc.sim>)

Mantel-Hubert spatial general cross product statistic

lextrB() lextrW() lextrS() griffith_sone() subgraph_eigenw() mom_calc() mom_calc_int2() stsls() impacts(<stsls>) GMerrorsar() summary(<gmsar>) GMargminImage() gstsls() impacts(<gmsar>) Hausman.test(<gmsar>) lagmess() ME() SpatialFiltering() LR.sarlm() logLik(<sarlm>) LR1.sarlm() Wald1.sarlm() Hausman.test(<sarlm>) as.spam.listw() as_dgRMatrix_listw() as_dsTMatrix_listw() as_dsCMatrix_I() as_dsCMatrix_IrW() Jacobian_W() powerWeights() plot(<lagImpact>) print(<lagImpact>) summary(<lagImpact>) HPDinterval(<lagImpact>) intImpacts() can.be.simmed() eigenw() similar.listw() do_ldet() jacobianSetup() cheb_setup() mcdet_setup() eigen_setup() eigen_pre_setup() spam_setup() spam_update_setup() Matrix_setup() Matrix_J_setup() LU_setup() LU_prepermutate_setup() moments_setup() SE_classic_setup() SE_whichMin_setup() SE_interp_setup() MCMCsamp() spautolm() summary(<spautolm>) spBreg_sac() impacts(<MCMC_sar_g>) impacts(<MCMC_sem_g>) impacts(<MCMC_sac_g>) spBreg_err() spBreg_lag() predict(<SLX>) lmSLX() impacts(<SLX>) create_WX() anova(<sarlm>) bptest.sarlm() errorsarlm() impacts(<sarlm>) lagsarlm() predict(<sarlm>) print(<sarlm.pred>) as.data.frame(<sarlm.pred>) residuals(<sarlm>) deviance(<sarlm>) coef(<sarlm>) vcov(<sarlm>) fitted(<sarlm>) sacsarlm() summary(<sarlm>) print(<sarlm>) print(<summary.sarlm>) trW()

Deprecated Functions in Package spdep

spdep()

Return package version number

spweights.constants() Szero()

Provides constants for spatial weights matrices

ssw()

Compute the sum of dissimilarity

subset(<listw>)

Subset a spatial weights list

subset(<nb>)

Subset a neighbours list

summary(<nb>) print(<nb>) summary(<listw>) print(<listw>)

Print and summary function for neighbours and weights lists

is.symmetric.nb() sym.attr.nb() make.sym.nb() old.make.sym.nb() is.symmetric.glist()

Test a neighbours list for symmetry

tolerance.nb()

Function to construct edges based on a tolerance angle and a maximum distance

tri2nb()

Neighbours list from tri object

write.nb.gal()

Write a neighbours list as a GAL lattice file