Implements Collective And Point Anomaly (CAPA) <arXiv:1806.01947>, Multi-Variate Collective And Point Anomaly (MVCAPA) <arXiv:1909.01691>, Proportion Adaptive Segment Selection (PASS) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) <doi:10.1214/16-BA998> methods for the detection of anomalies in time series data. Also includes sequential versions of CAPA and MVCAPA <arXiv:2009.06670>.

c(person("Alex","Fisch",email="a.t.fisch@lancaster.ac.uk",role=c("aut")),
person("Daniel","Grose",email="dan.grose@lancaster.ac.uk",role=c("aut","cre")),
person("Lawrence","Bardwell",email="l.bardwell@lancaster.ac.uk",role=c("aut","ctb")),
person("Idris","Eckley",email="i.eckley@lancaster.ac.uk",role=c("aut","ths")),
person("Paul","Fearnhead",email="p.fearnhead@lancaster.ac.uk",role=c("aut","ths")))
Alex Fisch [aut],
Daniel Grose [aut, cre],
Lawrence Bardwell [aut, ctb],
Idris Eckley [aut, ths],
Paul Fearnhead [aut, ths]

Implements Collective And Point Anomaly (CAPA) <arXiv:1806.01947>, Multi-Variate Collective And Point Anomaly (MVCAPA) <arXiv:1909.01691>, Proportion Adaptive Segment Selection (PASS) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) <arXiv:1412.5565> methods for the detection of anomalies in time series data. Also includes sequential versions of CAPA and MVCAPA <arXiv:2009.06670>.

Implementation of Augmented Network Log Anomaly Detection
Procedures
Authors@R: c(
person("Bradley", "Boehmke", email = "bradleyboehmke@gmail.com", role = c("aut", "cre")),
person("Robert", "Gutierrez", email = "rjgutierrez2015@gmail.com", role = c("aut", "cre"),
person("Kenneth", "Bauer", email = "kenneth.bauer@afit.edu", role = c("ctb"),
person("Trevor", "Bihl", email = "trevor.bihl.2@us.af.mil", role = c("ctb"),
person("Cade", "Saie", email = "cade.saie@gmail.com", role = c("ctb")) )
Implements procedures to aid in detecting network log anomalies.
By combining various multivariate analytic approaches relevant to network
anomaly detection, it provides cyber analysts efficient means to detect
suspected anomalies requiring further evaluation.
stats, MASS, magrittr, plyr, gmp, caret, matrixStats,
qdapTools, dplyr, purrr, tibble, RColorBrewer, tidyverse,
gplots