Learning mixtures of polynomials of multidimensional probability densities from data using B-splines

Rmop: Multidimensional mixture of polynomials learning from data

A mixture of polynomials (MoPs) is a piecewise polynomial function that approximates a probability density over a closed domain. This package implements a method for learning MoP approximations of probability densities from data based on B-spline interpolation. One-dimensional and multidimensional MoPs can be obtained from data. Methods for manipulating and plotting MoPs are provided.

Info:

Version: 0.4-3
Depends: R (= 2.10), boot
Published: 2011-07-23
Author: Ulric Lund, Claudio Agostinelli
Maintainer: Claudio Agostinelli
License: GPL-2
Citation: circular citation info
In views: Environmetrics
CRAN checks: circular results
Downloads:
Package source: circular_0.4-3.tar.gz
MacOS X binary: circular_0.4-3.tgz
Windows binary: circular_0.4-3.zip
Reference manual: circular.pdf
News/ChangeLog: ChangeLog
Old sources: circular archive
  • Version: 1.0
  • Published: 2013-03-07
  • Author: Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga
  • Maintainer: Pedro L. López-Cruz
  • License: LGPL-3
  • Citation:
    • López-Cruz, P. L., Bielza, C., Larrañaga, P. Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation. International Journal of Approximate Reasoning, in press.PDF
    • López-Cruz, P. L., Bielza, C., Larrañaga, P., Learning mixtures of polynomials from data using B-spline interpolation, in: Cano, A., Gómez-Olmedo, M., Nielsen, T. D. (Eds.), Proceedings of the 6th European Workshop on Probabilistic Graphical Models (PGM2012), 2012, pp. 211-218. PDF
  • Depends: mnormt, polynom, multipol, rgl, cubature, RColorBrewer

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