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smerc (version 1.4.2)

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

1.4.2

License

GPL (>= 2)

Maintainer

Joshua French

Last Published

February 13th, 2021

Functions in smerc (1.4.2)

Compute region weights for cepp.test

Perform cepp.test on simulated data

Cluster Evalation Permutation Procedure Test

Double Connection spatial scan test

Perform dc.test on simulated data

Determine zones for the Dynamic Minimum Spanning Tree scan test

Dynamic Minimum Spanning Tree spatial scan test

Distinct elements of a list

Nearest neighbors for elliptic scan

Combine distinct zones

Elliptical Spatial Scan Test

Determine zones for elliptic.test

Perform elliptic.test on simulated data

Construct connected subgraphs

SpatialPolygonsDataFrame for New York leukemia data.

Besag-Newell Test

Determine case windows (circles)

Perform mlink.test on simulated data

Determine zones for flexibly shaped spatial scan test

Flexibly-shaped Spatial Scan Test

Maximum Linkage spatial scan test

all_shape_dists

Return all shapes and distances for each zone

Perform dmst.test on simulated data

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

Compute Elbow Point

Convert logical vector to zone

Restricted Flexibly-shaped Spatial Scan Test

Compute Monte Carlo p-value

Perform edmst.test on simulated data

Early Stopping Dynamic Minimum Spanning Tree spatial scan test

Optimal Population Upper Bound Statistics

Adjacency matrix for New York leukemia data.

Determine zones for the Maximum Linkage scan test

Prepare smerc_cluster

Determine zones for flexibly shaped spatial scan test

Determine zones for flexibly shaped spatial scan test

Minimum spanning tree for all regions

summary.smerc_cluster

Summary of smerc_cluster object

arg_check_dist_ellipse

Check argments if dist.ellipse

Spatial scan statistic

Upper Level Set Spatial Scan Test

Determine sequence of ULS zones.

Determine sequence of fast subset scan zones

print.smerc_cluster

Print object of class smerc_cluster.

K nearest neighbors

Compute minor axis distance of ellipse

Determine zones for the Double Connected scan test

Spatial Scan Test

Apply getElement over a list

Fast Subset Scan Test

Perform fast.test on simulated data

Determine zones for the maxima likelihood first algorithm.

Maxima Likelihood First Scan Test

Convert nearest neighbors list to zones

Flexibly-shaped Spatial Scan Test

Perform flex.test on simualated data

Minimum spanning tree sequence

Determine zones for flexibly shaped spatial scan test

Determine nearest neighbors based on maximum distance

Leukemia data for 281 regions in New York.

print.smerc_optimal_ubpop

Print object of class smerc_optimal_ubpop.

Determine duplicates in nearest neighbor list

Cumulative sum over nearest neighbors

Perform scan test on simulated data sequentially

Determine non-overlapping zones

Perform rflex.test on simualated data

Sequential Scan Test

Determine nearest neighbors with population constraint

Plots an object of class tango.

plot.smerc_cluster

Plot object of class smerc_cluster.

Print object of class tango.

Return nicely formatted results from mst.all

Perform scan.test on simulated data

Tango's statistic

plot.smerc_optimal_ubpop

Plot object of class smerc_optimal_ubpop.

Tango's clustering detection test

Distance-based weights for tango.test

Return most significant, non-overlapping zones

Perform uls.test on simulated data

Compute middle p-value

Determine zones for the spatial scan test

Spatial scan statistic

Returns segments connecting neighbors