Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Jonas M. B. Haslbeck, Lourens J. Waldorp
Title: mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data
Abstract: We present the R package mgm for the estimation of k-order mixed graphical models (MGMs) and mixed vector autoregressive (mVAR) models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since data sets consisting of mixed types of variables (continuous, count, categorical) are ubiquitous. In addition, we allow to relax the stationarity assumption of both models by introducing time-varying versions of MGMs and mVAR models based on a kernel weighting approach. Time-varying models offer a rich description of temporally evolving systems and allow to identify external influences on the model structure such as the impact of interventions. We provide the background of all implemented methods and provide fully reproducible examples that illustrate how to use the package.

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Paper: mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data     Download PDF (Downloads: 295)
mgm_1.2-9.tar.gz: R source package Download (Downloads: 24; 589KB) Replication materials Download (Downloads: 22; 72KB)

DOI: 10.18637/jss.v093.i08

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.