Dear R users,
It is my pleasure to announce the availability of package stepR (1.0-2)
on CRAN.
The main purpose of the package is to fit piecewise constant functions
(a.k.a. step-functions or block signals) to serial data in a fully
data-driven manner under certain (Gaussian or non-Gaussian)
distributional assumptions.
It mainly implements the algorithms described in the references below -
in a (hopefully) user-friendly fashion.
Try
library(stepR)
example(smuceR) # for [1] and [2]
example(jsmurf) # for [3]
example(stepsel) # for [4]
to get an idea about what it can do, and how to use it.
We hope it proves useful; community feedback is therefore very welcome!
Best regards
Thomas Hotz
TU Ilmenau, Institute of Mathematics
References:
[1] Frick, K., Munk, A., and Sieling, H. (2014). Multiscale Change-Point
Inference. With discussion and rejoinder by the authors. Journal of the
Royal Statistical Society, Series B, 76(3), 495-580.
[2] Futschik, A., Hotz, T., Munk, A. Sieling, H. (2014). Multiresolution
DNA partitioning: statistical evidence for segments. Bioinformatics,
30(16), 2255-2262.
[3] Hotz, T., Sch?tte, O., Sieling, H., Polupanow, T., Diederichsen, U.,
Steinem, C., and Munk, A. (2013). Idealizing Ion Channel Recordings by a
Jump Segmentation Multiresolution Filter. IEEE Transactions on
NanoBioscience, 12(4), 376-386.
[4] Boysen, L., Kempe, A., Liebscher, V., Munk, A., Wittich, O. (2009).
Consistencies and rates of convergence of jump-penalized least squares
estimators. The Annals of Statistics, 37(1), 157-183.
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