Parametric and Nonparametric Sequential Change Detection in R: The cpm Package
نویسنده
چکیده
The change point model (CPM) framework introduced in Hawkins, Qiu, and Kang (2003) and Hawkins and Zamba (2005) provides an effective and computationally efficient method for sequentially detecting multiple changes points in streams of Gaussian random variables, when no information is available regarding the parameters of the distribution in the various segments. It has since been extended in various ways by Hawkins and Deng (2010), Ross, Adams, Tasoulis, and Hand (2011) and Ross and Adams (2011b) to allow for fully nonparametric change detection in non-Gaussian sequences, when no knowledge is available regarding even the distributional form of the sequence. Another extension comes from Ross and Adams (2011a) which allows change detection in streams of Bernoulli random variables, again when the values of the parameters are unknown. This paper describes the R package cpm, which provides a fast implementation of all the above change point models. Its usage is illustrated through numerous examples.
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