Estimating psychological networks via Information Filtering Networks

TL;DR: This blog post summarizes and discusses Information Filtering Networks introduced in a new paper by Christensen et al. 2018, and discusses them compared to the lasso-regularized Markov Random Fields. The post ends with an R tutorial on how to estimate such networks. Markov Random Fields (MRF) have quickly become …

FAQ on network stability, part II: Why is my network unstable?

In a previous tutorial blog post, I summarized the bootstrapping routine developed by Sacha Epskamp and me that we recommend for testing the stability / accuracy / precision of estimated network models and parameters. This matters a great deal and should be considered a standard step in every empirical network …

Does network connectivity predict future depression? Non-replication paper published

Lizanne Schweren, a Postdoc at the University Medical Center Groningen, and colleagues published a paper in JAMA Psychiatry this morning showing that the connectivity of depression symptoms is, in contrast to what a prior paper found, not a predictor of treatment response. This is the first non-replication in the psychopathology …

Tutorial: how to review psychopathology network papers

The network literature on psychopathology is exploding, which means there are many reviews to be performed. I’m sure colleagues such as Denny Borsboom or Angélique Cramer have read and reviewed many more papers over the years than I did, but I did my share: 46 reviews since 2015. Many of …

R tutorial: how to identify communities of items in networks

A problem we see in psychological network papers is that authors sometimes over-interpret the visualization of their data. This pertains especially to the layout and node placement of the graph, for instance: do nodes in the networks cluster in certain communities. Below I will discuss this problem in some detail, …