Package: smerc 1.8.4

smerc: Statistical Methods for Regional Counts

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) <doi:10.1002/sim.4780140809> and Kulldorff (1997) <doi:10.1080/03610929708831995>.

Authors:Joshua French [aut, cre], Mohammad Meysami [ctb]

smerc_1.8.4.tar.gz
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smerc_1.8.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
smerc/json (API)

# Install 'smerc' in R:

install.packages('smerc', repos = c('https://jfrench.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jfrench/smerc/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

neast - Breast cancer mortality in the Northeastern United States
neastw - Binary adjacency matrix for 'neast'
nydf - Leukemia 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.
nyw - Adjacency matrix for New York leukemia data.

On CRAN:

cpp

6.04 score 3 stars 2 packages 61 scripts 916 downloads 106 exports 3 dependencies

Last updated from:fb7d17c55c. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	229
linux-devel-x86_64	OK	287
source / vignettes	OK	211
linux-release-arm64	OK	258
linux-release-x86_64	OK	224
macos-release-arm64	OK	200
macos-release-x86_64	OK	362
macos-oldrel-arm64	OK	201
macos-oldrel-x86_64	OK	501
windows-devel	OK	250
windows-release	OK	195
windows-oldrel	OK	256
wasm-release	OK	146

Exports:all_shape_dists arg_check_dist_ellipse bn.test bn.zones casewin cepp.sim cepp.test cepp.weights clusters color.clusters combine.zones csg2 dc.sim dc.test dc.zones dist.ellipse distinct dmst.sim dmst.test dmst.zones dweights edmst.sim edmst.test edmst.zones elbow_point elliptic.nn elliptic.penalty elliptic.sim elliptic.sim.adj elliptic.test elliptic.zones eucdist fast.sim fast.test fast.zones flex_test flex_zones flex.sim flex.test flex.zones gcdist gedist knn lcsg2 lget lgetElement logical2zones mc.pvalue mlf.test mlf.zones mlink.sim mlink.test mlink.zones morancr.sim morancr.stat morancr.test mst.all mst.seq nclusters nn.cumsum nn2zones nndist nndup nnpop noc_enn noc_nn noz optimal_ubpop precog.sim precog.test prep.mst rflex_zones rflex.midp rflex.sim rflex.test rflex.zones scan_stat scan.nn scan.sim scan.sim.adj scan.stat scan.test scan.zones scsg2 seq_scan_sim seq_scan_test sget sgetElement sig_noc sig_prune smerc_cluster stat_binom stat_binom_adj stat_poisson stat_poisson_adj stat.binom stat.poisson stat.poisson.adj tango.stat tango.test tango.weights uls.sim uls.test uls.zones w2segments zones.sum

Dependencies:pbapply Rcpp RcppProgress

smerc-demo

Introduction | Scan methods | Other methods | Additional details about cluster detection methods | References

Rendered fromsmerc_demo.Rmdusingknitr::rmarkdown

Last update: 2023-07-07
Started: 2021-01-14

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Besag-Newell Test	bn.test
Determine case windows (circles)	bn.zones casewin
Perform 'cepp.test' on simulated data	cepp.sim
Cluster Evalation Permutation Procedure Test	cepp.test
Compute region weights for 'cepp.test'	cepp.weights
Extract clusters	clusters
Color clusters	color.clusters
Combine distinct zones	combine.zones
Construct connected subgraphs	csg2 lcsg2 scsg2
Perform 'dc.test' on simulated data	dc.sim
Double Connection spatial scan test	dc.test
Determine zones for the Double Connected scan test	dc.zones
Compute minor axis distance of ellipse	dist.ellipse
Distinct elements of a list	distinct
Perform 'dmst.test' on simulated data	dmst.sim
Dynamic Minimum Spanning Tree spatial scan test	dmst.test
Determine zones for the Dynamic Minimum Spanning Tree scan test	dmst.zones
Perform 'edmst.test' on simulated data	edmst.sim
Early Stopping Dynamic Minimum Spanning Tree spatial scan test	edmst.test
Determine zones for the early stopping dynamic Minimum Spanning Tree scan test	edmst.zones
Compute Elbow Point	elbow_point
Nearest neighbors for elliptic scan	elliptic.nn
Compute elliptic penalty	elliptic.penalty
Perform 'elliptic.test' on simulated data	elliptic.sim.adj
Elliptical Spatial Scan Test	elliptic.test
Determine zones for 'elliptic.test'	elliptic.zones
Perform 'fast.test' on simulated data	fast.sim
Fast Subset Scan Test	fast.test
Determine sequence of fast subset scan zones	fast.zones
Flexibly-shaped Spatial Scan Test	flex_test
Determine zones for flexibly shaped spatial scan test	flex_zones
Perform 'flex.test' on simualated data	flex.sim
Flexibly-shaped Spatial Scan Test	flex.test
Determine zones for flexibly shaped spatial scan test	flex.zones
Compute distance for geographic coordinates	eucdist gcdist gedist
K nearest neighbors	knn
Apply getElement over a list	lget lgetElement sget sgetElement
Maxima Likelihood First Scan Test	mlf.test
Determine zones for the maxima likelihood first algorithm.	mlf.zones
Perform 'mlink.test' on simulated data	mlink.sim
Maximum Linkage spatial scan test	mlink.test
Determine zones for the Maximum Linkage scan test	mlink.zones
Constant-risk Moran's I statistic	morancr.sim
Constant-risk Moran's I statistic	morancr.stat
Constant-risk Moran's I-based test	morancr.test
Minimum spanning tree for all regions	mst.all
Minimum spanning tree sequence	mst.seq
Number of clusters	nclusters
Breast cancer mortality in the Northeastern United States	neast
Binary adjacency matrix for 'neast'	neastw
Cumulative sum over nearest neighbors	nn.cumsum
Convert nearest neighbors list to zones	nn2zones
Determine nearest neighbors based on maximum distance	nndist
Determine duplicates in nearest neighbor list	nndup
Determine nearest neighbors with population constraint	nnpop scan.nn
Determine non-overlapping zones	noz
Leukemia data for 281 regions in New York.	nydf
'SpatialPolygons' object for New York leukemia data.	nypoly
'sf' object for New York leukemia data.	nysf
'SpatialPolygonsDataFrame' for New York leukemia data.	nysp
Adjacency matrix for New York leukemia data.	nyw
Optimal Population Upper Bound Statistics	optimal_ubpop
Plot object of class 'smerc_cluster'.	plot.smerc_cluster
Plot object of class 'smerc_optimal_ubpop'.	plot.smerc_optimal_ubpop
Plots an object of class 'tango'.	plot.tango
Perform 'precog.test' on simulated data.	precog.sim
PreCoG Scan Test	precog.test
Print object of class 'smerc_cluster'.	print.smerc_cluster
Print object of class 'smerc_optimal_ubpop'.	print.smerc_optimal_ubpop
Print object of class 'smerc_similarity_test'.	print.smerc_similarity_test
Print object of class 'tango'.	print.tango
Determine zones for flexibly shaped spatial scan test	rflex_zones
Compute middle p-value	rflex.midp
Perform 'rflex.test' on simualated data	rflex.sim
Restricted Flexibly-shaped Spatial Scan Test	rflex.test
Determine zones for flexibly shaped spatial scan test	rflex.zones
Spatial scan statistic	scan_stat stat_binom stat_poisson
Perform 'scan.test' on simulated data	scan.sim.adj
Spatial scan statistic	scan.stat stat.binom stat.poisson
Spatial Scan Test	scan.test
Determine zones for the spatial scan test	scan.zones
Return most significant, non-overlapping zones	sig_noc
smerc	smerc-package smerc
Prepare 'smerc_cluster'	smerc_cluster
Summary of 'smerc_cluster' object	summary.smerc_cluster
Tango's statistic	tango.stat
Tango's clustering detection test	tango.test
Distance-based weights for 'tango.test'	dweights tango.weights
Perform 'uls.test' on simulated data	uls.sim
Upper Level Set Spatial Scan Test	uls.test
Determine sequence of ULS zones.	uls.zones
Sum over zones	zones.sum