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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
SD.RStests()
Rao's score and adjusted Rao's score tests of linear hypotheses for spatial Durbin and spatial Durbin error models
aggregate(<nb>)
Aggregate a spatial neighbours object
airdist()
Measure distance from plot
autocov_dist()
Distance-weighted autocovariate
bhicv
Data set with 4 life condition indices of Belo Horizonte region
card()
Cardinalities for neighbours lists
cell2nb() 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() addlinks1()
Drop and add 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
hotspot()
Cluster Classifications for Local Indicators of Spatial Association and Local Indicators for Categorical Data
include.self() remove.self()
Include self in neighbours list
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
licd_multi()
Local Indicators for Categorical Data
listw2sn() sn2listw()
Spatial neighbour sparse representation
lm.RStests() lm.LMtests() print(<RStestlist>) summary(<RStestlist>) print(<RStestlist.summary>)
Rao's score (a.k.a Lagrange Multiplier) diagnostics for spatial dependence in linear models
lm.morantest()
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
localC() localC_perm()
Compute Local Geary statistic
localG() localG_perm()
G and Gstar local spatial statistics
localGS()
A local hotspot statistic for analysing multiscale datasets
local_joincount_bv()
Calculate the local bivariate join count
local_joincount_uni()
Calculate the local univariate join count
localmoran() localmoran_perm()
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
localmoran_bv()
Compute the Local Bivariate Moran's I Statistic
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
moran_bv()
Compute the Global Bivariate Moran's I
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() listw2U()
Spatial weights for neighbours lists
nb2listwdist()
Distance-based 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.swmdbf2listw() read_swm_dbf() write.swmdbf() write_swm_dbf() write.sn2DBF()
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.SubgraphOption() get.SubgraphOption() set.SubgraphCeiling() get.SubgraphCeiling() set.NoNeighbourOption() get.NoNeighbourOption() 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
aple.plot() localAple() aple.mc() aple() lextrB() lextrW() lextrS() griffith_sone() subgraph_eigenw() mom_calc() mom_calc_int2() stsls() impacts(<stsls>) GMerrorsar() summary(<gmsar>) 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()
Defunct 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