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

TL;DR

The paper introduces the R-package mgm for estimating high-dimensional, time-varying mixed graphical and vector autoregressive models that handle diverse variable types and evolving data structures.

Contribution

It extends graphical models to mixed data types and incorporates time-varying structures using kernel weighting, enabling analysis of dynamic, high-dimensional systems.

Findings

Provides a flexible R-package for mixed data types

Enables modeling of non-stationary, evolving systems

Includes reproducible examples for practical use

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 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.