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A Tutorial on Estimating Time-Varying Vector Autoregressive Models
Jonas M B Haslbeck, Laura F Bringmann, Lourens J Waldorp
Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid t…
How well do Network Models predict Observations? On the Importance of Predictability in Network Models
Jonas Haslbeck, Lourens J Waldorp
Network models are an increasingly popular way to abstract complex psychological phenomena. While the study of the structure of network models has led to many important insights, l…
Mean field dynamics of graphs I: Evolution of probabilistic cellular automata for random and small-world graphs
Lourens J. Waldorp, Jolanda J. Kossakowski
It was recently shown how graphs can be used to provide descriptions of psychopathologies, where symptoms of, say, depression, affect each other and certain configurations determin…
Mean Field Dynamics of Graphs II: Assessing the Risk for the Development of Phase Transitions in Empirical Data
Jolanda J Kossakowski, Marijke CM Gordijn, Harriette Riese +1
Psychological disorders like major depressive disorder can be seen as complex dynamical systems. By looking at symptom activation patterns, we can investigate the dynamic behaviour…
Structure estimation for mixed graphical models in high-dimensional data
Jonas M. B. Haslbeck, Lourens J. Waldorp
Undirected graphical models are a key component in the analysis of complex observational data in a large variety of disciplines. In many of these applications one is interested in…
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data
Jonas M. B. Haslbeck, Lourens J. Waldorp
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…