We provide two algorithms for monitoring change points with online matrixvalued time series, under the assumption of a twoway 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 wellknown 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 nonspiked 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)<doi:10.48550/arXiv.2112.13479>.
Package details 


Author  Yong He [aut], Xinbing Kong [aut], Lorenzo Trapani [aut], Long Yu [aut, cre] 
Maintainer  Long Yu <fduyulong@163.com> 
License  GPL2  GPL3 
Version  0.1.2 
Package repository  View on CRAN 
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