Package: OLCPM Type: Package Title: Online Change Point Detection for Matrix-Valued Time Series Version: 0.1.2 Authors@R: c(person("Yong", "He", role=c("aut")), person("Xinbing", "Kong", role=c("aut")), person("Lorenzo", "Trapani", role=c("aut")), person("Long", "Yu", role=c("aut", "cre"), email = "fduyulong@163.com")) Author: Yong He [aut], Xinbing Kong [aut], Lorenzo Trapani [aut], Long Yu [aut, cre] Maintainer: Long Yu Description: We provide two algorithms for monitoring change points with online matrix-valued time series, under the assumption of a two-way factor structure. The algorithms are based on different calculations of the second moment matrices. One is based on stacking the columns of matrix observations, while another is by a more delicate projected approach. A well-known fact is that, in the presence of a change point, a factor model can be rewritten as a model with a larger number of common factors. In turn, this entails that, in the presence of a change point, the number of spiked eigenvalues in the second moment matrix of the data increases. Based on this, we propose two families of procedures - one based on the fluctuations of partial sums, and one based on extreme value theory - to monitor whether the first non-spiked eigenvalue diverges after a point in time in the monitoring horizon, thereby indicating the presence of a change point. This package also provides some simple functions for detecting and removing outliers, imputing missing entries and testing moments. See more details in He et al. (2021). License: GPL-2 | GPL-3 Encoding: UTF-8 Imports: LaplacesDemon, RSpectra Depends: R (>= 3.5.0) NeedsCompilation: no Packaged: 2026-07-03 08:24:13 UTC; root RoxygenNote: 7.3.1 LazyData: true Repository: https://fduyulong.r-universe.dev Date/Publication: 2024-05-31 02:40:33 UTC RemoteUrl: https://github.com/cran/OLCPM RemoteRef: HEAD RemoteSha: 31f5b4d8b7834c812933ac3b0ba0ff13efd85471