mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data

mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data

structure estimation, mixed graphical models, Markov random fields, dynamic
graphical models, time-varying graphical models, vector autoregressive models

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.

Jonas M. B. Haslbeck

https://www.jstatsoft.org/article/view/v093i08

University of Amsterdam

April 2020

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