Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering.
This package implements the techniques introduced in Einbeck, Tutz & Evers (2005), Einbeck, Evers & Powell (2010) and Einbeck (2011).
The main functions to be called by the user are
lpc, for the estimation of the local centers of mass
which describe the principal curve;
ms, for calculation of mean shift trajectories and associated clusters.
The package contains also specialized functions for projection and spline fitting (
lpc.spline), functions for bandwidth selection (
ms.self.coverage), goodness of fit assessment (
coverage), as well as some methods for generic functions such as
Contributions (in form of pieces of code, or useful suggestions for improvements) by Jo Dwyer, Mohammad Zayed, and Ben Oakley are gratefully acknowledged.
Jochen Einbeck and Ludger Evers
Maintainer: Jochen Einbeck <firstname.lastname@example.org>
Einbeck, J., Tutz, G., & Evers, L. (2005): Local principal curves, Statistics and Computing 15, 301-313.
Einbeck, J., Evers, L., & Powell, B. (2010): Data compression and regression through local principal curves and surfaces, International Journal of Neural Systems, 20, 177-192.
Einbeck, J. (2011): Bandwidth selection for nonparametric unsupervised learning techniques – a unified approach via self-coverage. Journal of Pattern Recognition Research 6, 175-192.
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