Detection of anomalous space-time clusters using the scan
statistics methodology. Focuses on prospective surveillance of data streams,
scanning for clusters with ongoing anomalies. Hypothesis testing is made
possible by Monte Carlo simulation. Allévius (2018) <doi:10.21105/joss.00515>.
| Version: |
1.1.1 |
| Depends: |
R (≥ 3.4) |
| Imports: |
dplyr, ismev, magrittr, plyr, Rcpp, stats, sets, tibble, tidyr |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
purrr, doParallel, foreach, ggplot2, knitr, MASS, pscl, reshape2, rmarkdown, sp, testthat, gamlss.dist |
| Published: |
2023-01-26 |
| DOI: |
10.32614/CRAN.package.scanstatistics |
| Author: |
Benjamin Allévius [aut],
Paul Romer Present [ctb, cre] |
| Maintainer: |
Paul Romer Present <paul.romerpresent at fastmail.fm> |
| BugReports: |
https://github.com/promerpr/scanstatistics/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/promerpr/scanstatistics |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++11 |
| Citation: |
scanstatistics citation info |
| Materials: |
README NEWS |
| CRAN checks: |
scanstatistics results |