The link to the package is <https://CRAN.R-project.org/package=onlineBcp>.

Title: Online Change Point Detection in High-Dimensional Covariance
Description: Implement a new stopping rule to detect anomaly in the covariance structure of high-dimensional online data. The detection procedure can be applied to Gaussian or non-Gaussian data with a large number of components. Moreover, it allows both spatial and temporal dependence in data. The dependence can be estimated by a data-driven procedure. The level of threshold in the stopping rule can be determined at a pre-selected average run length. More detail can be seen in Li, L. and Li, J. (2020) "Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks." <arXiv:1911.07762>.

Title: Detect Changepoints in Multivariate Time Series
Description: Detects multiple changepoints in uni- or multivariate time series data. The algorithm is based on Bayesian methods and detects changes on-line; i.e. the model updates with every observation rather than relying on retrospective segmentation. However, the user may choose to use the algorithm off- or on-line.

Title: Online Principal Component Analysis
Author: David Degras [aut, cre], Herve Cardot [ctb]
Description: Online PCA for multivariate and functional data using perturbation, incremental, and stochastic gradient methods.
Imports: rARPACK, Rcpp (>= 0.11.2), splines
LinkingTo: Rcpp, RcppArmadillo (>= 0.4.320.0)

Imports: Rcpp (>= 0.11.4), splines, stats

Description: Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.

Title: Online Fitting of Time-Adaptive Lasso Vector Auto Regression
Authors@R: c(person(given = "Jakob", family = "Messner", role = c("aut", "cre"), email = "jwmm@dtu.dk"))
Description: Functions for recursive online fitting of time-adaptive lasso vector auto regression. A recursive coordinate descent algorithm is used to estimate sparse vector auto regressive models and exponential forgetting is applied to allow model changes. Details can be found in Jakob W. Messner and Pierre Pinson (2018). "Online adaptive LASSO estimation in Vector Auto Regressive models for wind power forecasting in high dimension". International Journal of Forecasting, in press. Preprint: <http://pierrepinson.com/docs/MessnerPinson18.pdf>.