Package index
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oldcol
- Columbus OH spatial analysis data set - old numbering
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EBImoran.mc()
- Permutation test for empirical Bayes index
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EBest()
- Global Empirical Bayes estimator
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EBlocal()
- Local Empirical Bayes estimator
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LOSH()
- Local spatial heteroscedasticity
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LOSH.cs()
- Chi-square based test for local spatial heteroscedasticity
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LOSH.mc()
- Bootstrapping-based test for local spatial heteroscedasticity
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SD.RStests()
- Rao's score and adjusted Rao's score tests of linear hypotheses for spatial Durbin and spatial Durbin error models
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aggregate(<nb>)
- Aggregate a spatial neighbours object
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airdist()
- Measure distance from plot
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autocov_dist()
- Distance-weighted autocovariate
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bhicv
- Data set with 4 life condition indices of Belo Horizonte region
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card()
- Cardinalities for neighbours lists
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choynowski()
- Choynowski probability map values
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columbus
- Columbus OH spatial analysis data set
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n.comp.nb()
- Depth First Search on Neighbor Lists
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diffnb()
- Differences between neighbours lists
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dnearneigh()
- Neighbourhood contiguity by distance
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droplinks()
addlinks1()
- Drop and add links in a neighbours list
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edit(<nb>)
- Interactive editing of neighbours lists
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eire
- Eire data sets
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geary()
- Compute Geary's C
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geary.mc()
- Permutation test for Geary's C statistic
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geary.test()
- Geary's C test for spatial autocorrelation
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globalG.test()
- Global G test for spatial autocorrelation
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gabrielneigh()
relativeneigh()
soi.graph()
graph2nb()
plot(<Gabriel>)
plot(<relative>)
- Graph based spatial weights
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grid2nb()
- Construct neighbours for a GridTopology
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hotspot()
- Cluster Classifications for Local Indicators of Spatial Association and Local Indicators for Categorical Data
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include.self()
remove.self()
- Include self in neighbours list
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joincount.mc()
- Permutation test for same colour join count statistics
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joincount.multi()
print(<jcmulti>)
- BB, BW and Jtot join count statistic for k-coloured factors
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joincount.test()
print(<jclist>)
- BB join count statistic for k-coloured factors
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knearneigh()
- K nearest neighbours for spatial weights
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knn2nb()
- Neighbours list from knn object
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lag(<listw>)
- Spatial lag of a numeric vector
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lee()
- Compute Lee's statistic
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lee.mc()
- Permutation test for Lee's L statistic
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lee.test()
- Lee's L test for spatial autocorrelation
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licd_multi()
- Local Indicators for Categorical Data
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listw2sn()
sn2listw()
- Spatial neighbour sparse representation
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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
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lm.morantest()
- Moran's I test for residual spatial autocorrelation
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lm.morantest.exact()
print(<moranex>)
- Exact global Moran's I test
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lm.morantest.sad()
print(<moransad>)
summary(<moransad>)
print(<summary.moransad>)
- Saddlepoint approximation of global Moran's I test
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localC()
localC_perm()
- Compute Local Geary statistic
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localG()
localG_perm()
- G and Gstar local spatial statistics
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localGS()
- A local hotspot statistic for analysing multiscale datasets
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local_joincount_bv()
- Calculate the local bivariate join count
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local_joincount_uni()
- Calculate the local univariate join count
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localmoran()
localmoran_perm()
- Local Moran's I statistic
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localmoran.exact()
localmoran.exact.alt()
print(<localmoranex>)
as.data.frame(<localmoranex>)
- Exact local Moran's Ii tests
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localmoran.sad()
print(<localmoransad>)
summary(<localmoransad>)
print(<summary.localmoransad>)
listw2star()
- Saddlepoint approximation of local Moran's Ii tests
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localmoran_bv()
- Compute the Local Bivariate Moran's I Statistic
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mat2listw()
- Convert a square spatial weights matrix to a weights list object
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moran()
- Compute Moran's I
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moran.mc()
- Permutation test for Moran's I statistic
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moran.plot()
- Moran scatterplot
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moran.test()
- Moran's I test for spatial autocorrelation
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moran_bv()
- Compute the Global Bivariate Moran's I
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mstree()
- Find the minimal spanning tree
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nb2INLA()
- Output spatial neighbours for INLA
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nb2WB()
listw2WB()
- Output spatial weights for WinBUGS
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nb2blocknb()
- Block up neighbour list for location-less observations
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nb2lines()
listw2lines()
df2sn()
- Use vector files for import and export of weights
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nb2listw()
listw2U()
- Spatial weights for neighbours lists
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nb2listwdist()
- Distance-based spatial weights for neighbours lists
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nb2mat()
listw2mat()
- Spatial weights matrices for neighbours lists
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nbdists()
- Spatial link distance measures
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nblag()
nblag_cumul()
- Higher order neighbours lists
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intersect.nb()
union.nb()
setdiff.nb()
complement.nb()
- Set operations on neighborhood objects
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p.adjustSP()
- Adjust local association measures' p-values
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plot(<mst>)
- Plot the Minimum Spanning Tree
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plot(<nb>)
plot(<listw>)
- Plot a neighbours list
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plot(<skater>)
- Plot the object of skater class
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poly2nb()
- Construct neighbours list from polygon list
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probmap()
- Probability mapping for rates
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prunecost()
- Compute cost of prune each edge
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prunemst()
- Prune a Minimun Spanning Tree
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read.gal()
read.geoda()
- Read a GAL lattice file into a neighbours list
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read.gwt2nb()
write.sn2gwt()
read.dat2listw()
write.sn2dat()
read.swmdbf2listw()
read_swm_dbf()
write.sn2DBF()
- Read and write spatial neighbour files
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Rotation()
- Rotate a set of point by a certain angle
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set.mcOption()
get.mcOption()
set.coresOption()
get.coresOption()
set.ClusterOption()
get.ClusterOption()
- Options for parallel support
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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
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skater()
- Spatial 'K'luster Analysis by Tree Edge Removal
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sp.correlogram()
plot(<spcor>)
print(<spcor>)
- Spatial correlogram
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sp.mantel.mc()
plot(<mc.sim>)
- Mantel-Hubert spatial general cross product statistic
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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
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spdep()
- Return package version number
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spweights.constants()
Szero()
- Provides constants for spatial weights matrices
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ssw()
- Compute the sum of dissimilarity
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subset(<listw>)
- Subset a spatial weights list
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subset(<nb>)
- Subset a neighbours list
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summary(<nb>)
print(<nb>)
summary(<listw>)
print(<listw>)
- Print and summary function for neighbours and weights lists
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is.symmetric.nb()
sym.attr.nb()
make.sym.nb()
old.make.sym.nb()
is.symmetric.glist()
- Test a neighbours list for symmetry
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tolerance.nb()
- Function to construct edges based on a tolerance angle and a maximum distance
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tri2nb()
- Neighbours list from tri object
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write.nb.gal()
- Write a neighbours list as a GAL lattice file