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smerc (version 1.8.2)

Statistical Methods for Regional Counts

Description

Implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering. The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) and Kulldorff (1997) .

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Version

Install

install.packages('smerc')

Monthly Downloads

773

Version

1.8.2

License

GPL (>= 2)

Maintainer

Joshua French

Last Published

July 15th, 2023

Functions in smerc (1.8.2)

dc.sim

Perform dc.test on simulated data
csg2

Construct connected subgraphs
dmst.test

Dynamic Minimum Spanning Tree spatial scan test
distinct

Distinct elements of a list
dist.ellipse

Compute minor axis distance of ellipse
dmst.sim

Perform dmst.test on simulated data
dc.test

Double Connection spatial scan test
dmst.zones

Determine zones for the Dynamic Minimum Spanning Tree scan test
dc.zones

Determine zones for the Double Connected scan test
edmst.sim

Perform edmst.test on simulated data
elliptic.sim.adj

Perform elliptic.test on simulated data
edmst.test

Early Stopping Dynamic Minimum Spanning Tree spatial scan test
edmst.zones

Determine zones for the early stopping dynamic Minimum Spanning Tree scan test
elliptic.zones

Determine zones for elliptic.test
fast.sim

Perform fast.test on simulated data
elliptic.nn

Nearest neighbors for elliptic scan
elbow_point

Compute Elbow Point
elliptic.penalty

Compute elliptic penalty
knn

K nearest neighbors
elliptic.sim

Perform elliptic.test on simulated data
elliptic.test

Elliptical Spatial Scan Test
flex.sim

Perform flex.test on simualated data
flex.test

Flexibly-shaped Spatial Scan Test
flex_test

Flexibly-shaped Spatial Scan Test
flex.zones

Determine zones for flexibly shaped spatial scan test
logical2zones

Convert logical vector to zone
mlf.test

Maxima Likelihood First Scan Test
lget

Apply getElement over a list
flex_zones

Determine zones for flexibly shaped spatial scan test
gedist

Compute distance for geographic coordinates
mlink.test

Maximum Linkage spatial scan test
mlink.sim

Perform mlink.test on simulated data
morancr.stat

Constant-risk Moran's I statistic
morancr.test

Constant-risk Moran's I-based test
mlf.zones

Determine zones for the maxima likelihood first algorithm.
mc.pvalue

Compute Monte Carlo p-value
fast.test

Fast Subset Scan Test
fast.zones

Determine sequence of fast subset scan zones
mlink.zones

Determine zones for the Maximum Linkage scan test
mst.all

Minimum spanning tree for all regions
mst.seq

Minimum spanning tree sequence
nclusters

Number of clusters
nyw

Adjacency matrix for New York leukemia data.
optimal_ubpop

Optimal Population Upper Bound Statistics
nndist

Determine nearest neighbors based on maximum distance
nn2zones

Convert nearest neighbors list to zones
morancr.sim

Constant-risk Moran's I statistic
noz

Determine non-overlapping zones
noc_nn

Returned ordered non-overlapping clusters
neastw

Binary adjacency matrix for neast
nn.cumsum

Cumulative sum over nearest neighbors
print.tango

Print object of class tango.
print.smerc_similarity_test

Print object of class smerc_similarity_test.
precog.test

PreCoG Scan Test
prep.mst

Return nicely formatted results from mst.all
precog.sim

Perform precog.test on simulated data.
plot.tango

Plots an object of class tango.
print.smerc_cluster

Print object of class smerc_cluster.
print.smerc_optimal_ubpop

Print object of class smerc_optimal_ubpop.
plot.smerc_optimal_ubpop

Plot object of class smerc_optimal_ubpop.
plot.smerc_cluster

Plot object of class smerc_cluster.
noc_enn

Returned ordered non-overlapping clusters
nysp

SpatialPolygonsDataFrame for New York leukemia data.
nndup

Determine duplicates in nearest neighbor list
nysf

sf object for New York leukemia data.
scan.test

Spatial Scan Test
scan_stat

Spatial scan statistic
scan.zones

Determine zones for the spatial scan test
scan.stat

Spatial scan statistic
rflex_zones

Determine zones for flexibly shaped spatial scan test
tango.stat

Tango's statistic
nnpop

Determine nearest neighbors with population constraint
tango.test

Tango's clustering detection test
summary.smerc_cluster

Summary of smerc_cluster object
scan.sim.adj

Perform scan.test on simulated data
seq_scan_sim

Perform scan test on simulated data sequentially
sig_noc

Return most significant, non-overlapping zones
stat.poisson.adj

Compute Poisson test statistic
tango.weights

Distance-based weights for tango.test
uls.test

Upper Level Set Spatial Scan Test
nydf

Leukemia data for 281 regions in New York.
neast

Breast cancer mortality in the Northeastern United States
seq_scan_test

Sequential Scan Test
sig_prune

Prune significant, non-overlapping zones
scan.sim

Perform scan.test on simulated data
uls.zones

Determine sequence of ULS zones.
rflex.midp

Compute middle p-value
nypoly

SpatialPolygons object for New York leukemia data.
uls.sim

Perform uls.test on simulated data
rflex.zones

Determine zones for flexibly shaped spatial scan test
rflex.sim

Perform rflex.test on simualated data
rflex.test

Restricted Flexibly-shaped Spatial Scan Test
w2segments

Returns segments connecting neighbors
zones.sum

Sum over zones
smerc_cluster

Prepare smerc_cluster
smerc

smerc
clusters

Extract clusters
arg_check_dist_ellipse

Check argments if dist.ellipse
bn.test

Besag-Newell Test
combine.zones

Combine distinct zones
cepp.sim

Perform cepp.test on simulated data
cepp.test

Cluster Evalation Permutation Procedure Test
cepp.weights

Compute region weights for cepp.test
color.clusters

Color clusters
bn.zones

Determine case windows (circles)
all_shape_dists

Return all shapes and distances for each zone