multiple change point analysis simultaneously for mean AND variance WITHOUT distribution assumption in R [closed]

Is there a multiple change point analysis for both the mean and the variance simultaneously, implemented in R without a distribution assumption? I know the changepoint packet http://cran.r-project.org/web/packages/changepoint/index.html but as far as I see, it only allows change point analysis (mean & variance simultaneously) with distribution assumptions (normal, exponential, gamma).

closed as off-topic by Nick Cox, Peter Flom♦Aug 31 '17 at 11:27

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3 Answers

There may be some more general non-parametric methods in the cpm package: http://cran.r-project.org/web/packages/cpm/index.html. The manual associated with the package lists three methods that are supposedly used to check for more general distributional changes (such as simultaneous mean and variance changes). However, I haven't spent a lot of time on change-point analysis, so I can't speak for the quality of the package.

• @ Jonathan Thiele: Thank's! That looks fine. I will try it. – user11749 Jun 6 '12 at 20:54

There is also the ecp package which does more general nonparametric distributional changes and even allows for multivariate data:

http://cran.r-project.org/web/packages/ecp/

For those looking at this thread there is now also the changepoint.np package for R (available on CRAN) which contains a method for a change in distribution and is currently being revamped with more specific robust changepoint methods.