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Robustly Fitting Gaussian Graphical Models—the R Package robFitConGraph

In: Robust and Multivariate Statistical Methods

Author

Listed:
  • Daniel Vogel

    (MEDICE Arzneimittel Pütter GmbH & Co. KG
    University of Aberdeen, Institute for Complex Systems and Mathematical Biology)

  • Stuart J. Watt

    (Mirador Analytics)

  • Anna Wiedemann

    (University of Cambridge, Department of Psychiatry)

Abstract

This chapter gives a tutorial-style introduction to the R package robFitConGraph, which provides a robust goodness-of-fit test for Gaussian graphical models. Its use is demonstrated at a data example on music performance anxiety, which also illustrates why one would want to fit a Gaussian graphical model—and why one should do so robustly. The underlying theory is briefly explained, much of which has been developed by David Tyler.

Suggested Citation

  • Daniel Vogel & Stuart J. Watt & Anna Wiedemann, 2023. "Robustly Fitting Gaussian Graphical Models—the R Package robFitConGraph," Springer Books, in: Mengxi Yi & Klaus Nordhausen (ed.), Robust and Multivariate Statistical Methods, pages 277-296, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-22687-8_13
    DOI: 10.1007/978-3-031-22687-8_13
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