Man pages for smerc
Statistical Methods for Regional Counts

all_shape_distsReturn all shapes and distances for each zone
arg_check_dist_ellipseCheck argments if dist.ellipse
bn.testBesag-Newell Test
bn.zonesDetermine case windows (circles)
cepp.simPerform 'cepp.test' on simulated data
cepp.testCluster Evalation Permutation Procedure Test
cepp.weightsCompute region weights for 'cepp.test'
clustersExtract clusters
color.clustersColor clusters
combine.zonesCombine distinct zones
csgConstruct connected subgraphs
dc.simPerform 'dc.test' on simulated data
dc.testDouble Connection spatial scan test
dc.zonesDetermine zones for the Double Connected scan test
dist.ellipseCompute minor axis distance of ellipse
distinctDistinct elements of a list
dmst.simPerform 'dmst.test' on simulated data
dmst.testDynamic Minimum Spanning Tree spatial scan test
dmst.zonesDetermine zones for the Dynamic Minimum Spanning Tree scan...
edmst.simPerform 'edmst.test' on simulated data
edmst.testEarly Stopping Dynamic Minimum Spanning Tree spatial scan...
edmst.zonesDetermine zones for the early stopping dynamic Minimum...
elbow_pointCompute Elbow Point
elliptic.nnNearest neighbors for elliptic scan
elliptic.penaltyCompute elliptic penalty
elliptic.simPerform 'elliptic.test' on simulated data
elliptic.sim.adjPerform 'elliptic.test' on simulated data
elliptic.testElliptical Spatial Scan Test
elliptic.zonesDetermine zones for 'elliptic.test'
fast.simPerform 'fast.test' on simulated data
fast.testFast Subset Scan Test
fast.zonesDetermine sequence of fast subset scan zones
flex.simPerform 'flex.test' on simualated data
flex_testFlexibly-shaped Spatial Scan Test
flex.testFlexibly-shaped Spatial Scan Test
flex_zonesDetermine zones for flexibly shaped spatial scan test
flex.zonesDetermine zones for flexibly shaped spatial scan test
gedistCompute distance for geographic coordinates
knnK nearest neighbors
lgetApply getElement over a list
logical2zonesConvert logical vector to zone
mc.pvalueCompute Monte Carlo p-value
mlf.testMaxima Likelihood First Scan Test
mlf.zonesDetermine zones for the maxima likelihood first algorithm.
mlink.simPerform 'mlink.test' on simulated data
mlink.testMaximum Linkage spatial scan test
mlink.zonesDetermine zones for the Maximum Linkage scan test
morancr.simConstant-risk Moran's I statistic
morancr.statConstant-risk Moran's I statistic
morancr.testConstant-risk Moran's I-based test
mst.allMinimum spanning tree for all regions
mst.seqMinimum spanning tree sequence
nclustersNumber of clusters
neastBreast cancer mortality in the Northeastern United States
neastwBinary adjacency matrix for 'neast'
nn2zonesConvert nearest neighbors list to zones
nn.cumsumCumulative sum over nearest neighbors
nndistDetermine nearest neighbors based on maximum distance
nndupDetermine duplicates in nearest neighbor list
noc_ennReturned ordered non-overlapping clusters
noc_nnReturned ordered non-overlapping clusters
nozDetermine non-overlapping zones
nydfLeukemia data for 281 regions in New York.
nypoly'SpatialPolygons' object for New York leukemia data.
nysf'sf' object for New York leukemia data.
nysp'SpatialPolygonsDataFrame' for New York leukemia data.
nywAdjacency matrix for New York leukemia data.
optimal_ubpopOptimal Population Upper Bound Statistics
plot.smerc_clusterPlot object of class 'smerc_cluster'.
plot.smerc_optimal_ubpopPlot object of class 'smerc_optimal_ubpop'.
plot.tangoPlots an object of class 'tango'.
precog.simPerform 'precog.test' on simulated data.
precog.testPreCoG Scan Test
prep.mstReturn nicely formatted results from mst.all
print.smerc_clusterPrint object of class 'smerc_cluster'.
print.smerc_optimal_ubpopPrint object of class 'smerc_optimal_ubpop'.
print.smerc_similarity_testPrint object of class 'smerc_similarity_test'.
print.tangoPrint object of class 'tango'.
rflex.midpCompute middle p-value
rflex.simPerform 'rflex.test' on simualated data
rflex.testRestricted Flexibly-shaped Spatial Scan Test
rflex_zonesDetermine zones for flexibly shaped spatial scan test
rflex.zonesDetermine zones for flexibly shaped spatial scan test
scan.nnDetermine nearest neighbors with population constraint
scan.simPerform 'scan.test' on simulated data
scan.sim.adjPerform 'scan.test' on simulated data
scan_statSpatial scan statistic
scan.statSpatial scan statistic
scan.testSpatial Scan Test
scan.zonesDetermine zones for the spatial scan test
seq_scan_simPerform scan test on simulated data sequentially
seq_scan_testSequential Scan Test
sig_nocReturn most significant, non-overlapping zones
sig_prunePrune significant, non-overlapping zones
smerc_clusterPrepare 'smerc_cluster'
stat.poisson.adjCompute Poisson test statistic
summary.smerc_clusterSummary of 'smerc_cluster' object
tango.statTango's statistic
tango.testTango's clustering detection test
tango.weightsDistance-based weights for 'tango.test'
uls.simPerform 'uls.test' on simulated data
uls.testUpper Level Set Spatial Scan Test
uls.zonesDetermine sequence of ULS zones.
w2segmentsReturns segments connecting neighbors
zones.sumSum over zones
smerc documentation built on Oct. 10, 2023, 5:07 p.m.