MFT: The Multiple Filter Test for Change Point Detection

Provides statistical tests and algorithms for the detection of change points in time series and point processes - particularly for changes in the mean in time series and for changes in the rate and in the variance in point processes. References - Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014), A multiple filter test for the detection of rate changes in renewal processes with varying variance <doi:10.1214/14-AOAS782>. Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017), Multi-scale detection of variance changes in renewal processes in the presence of rate change points <doi:10.1111/jtsa.12254>. Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017), Multi-scale detection of rate changes in spike trains with weak dependencies <doi:10.1007/s10827-016-0635-3>. Michael Messer, Stefan Albert and Gaby Schneider (2018), The multiple filter test for change point detection in time series <doi:10.1007/s00184-018-0672-1>. Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+) Peak detection in time series.

Version: 2.0
Published: 2019-03-11
Author: Michael Messer, Stefan Albert, Solveig Plomer, Gaby Schneider
Maintainer: Michael Messer <messer at math.uni-frankfurt.de>
License: GPL-3
NeedsCompilation: no
CRAN checks: MFT results

Downloads:

Reference manual: MFT.pdf
Package source: MFT_2.0.tar.gz
Windows binaries: r-devel: MFT_2.0.zip, r-release: MFT_2.0.zip, r-oldrel: MFT_2.0.zip
OS X binaries: r-release: MFT_2.0.tgz, r-oldrel: MFT_2.0.tgz
Old sources: MFT archive

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