自回归模型
范畴变量
计算机科学
加权
核(代数)
图形模型
数据类型
变量(数学)
数据挖掘
算法
数学
人工智能
机器学习
统计
程序设计语言
放射科
数学分析
组合数学
医学
作者
Jonas M B Haslbeck,Lourens Waldorp
标识
DOI:10.18637/jss.v093.i08
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.
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