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

By Jonas M. B. Haslbeck, Lourens J. Waldorp. Journal of Statistical Software, 2015

770 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e2458401a8e11d74a48adeddb04009f499ebf787

How researchers see this paper

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In Artificial Intelligence

1. Ali Shojaie
2. Emily B. Fox
3. Chunyuan Li
4. Aaron Klein
5. David M. Blei
6. Matthew D. Hoffman

In Statistics and Probability

1. Ali Shojaie
2. Emily B. Fox
3. Chunyuan Li
4. Aaron Klein
5. Matthew D. Hoffman
6. Changyou Chen

Citations in the corpus

• Granger Causality: A Review and Recent Advances (2021, methodology): “Some of these developments have also been implemented in publicly available software packages, including mgm (Haslbeck and Waldorp, 2020), bigvar (Nicholson et al., 2017a) and ngc (Etzel and Shojaie, 2016).”