Until recently, large individual-level longitudinal data were unavailable to investigate clusters of disease, driving a need for suitable statistical tools. We introduce a robust, efficient, intuitive R package, ClustR
, for space–time cluster analysis
of individual-level data.
We developed ClustR
and evaluated the tool using a simulated dataset mirroring the population of California with constructed clusters. We assessed Cluster’s performance under various conditions and compared it with another space–time clustering
mostly exhibited high sensitivity for urban clusters and low sensitivity for rural clusters. Specificity was generally high. Compared with SaTScan
ran faster and demonstrated similar sensitivity, but had lower specificity. Select cluster types were detected better by ClustR
and vice versa.
is a user-friendly, publicly available tool designed to perform efficient cluster analysis
on individual-level data, filling a gap among current tools. ClustR
exhibited different strengths and may be useful in conjunction.