Statistics

Algorithm

Combinatorics

Machine learning

Data mining

Radiology

Mathematical analysis

Data type

Artificial intelligence

Graphical model

Autoregressive model

Variable (mathematics)

Weighting

Programming language

Mathematics

Computer science

Categorical variable

Medicine

Kernel (algebra)

Journal of Statistical Software

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.

<b>mgm</b>: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data

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