文章基本信息

标题：mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data
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作者：Jonas M. B. Haslbeck ; Lourens J. Waldorp
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2020
卷号：93
期号：1
页码：1-46
DOI：10.18637/jss.v093.i08
出版社：University of California, Los Angeles
摘要：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.
关键词：structure estimation;mixed graphical models;Markov random fields;dynamic graphical models;time-varying graphical models;vector autoregressive models.
其他关键词：structure estimation;mixed graphical models;Markov random fields;dynamic graphical models;time-varying graphical models;vector autoregressive models