  We discuss the Gaussian graphical model (GGM; an undirected network of
partial correlation coefficients) and detail its utility as an exploratory data
analysis tool. The GGM shows which variables predict one-another, allows for
sparse modeling of covariance structures, and may highlight potential causal
relationships between observed variables. We describe the utility in 3 kinds of
psychological datasets: datasets in which consecutive cases are assumed
independent (e.g., cross-sectional data), temporally ordered datasets (e.g., n
= 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In
time-series analysis, the GGM can be used to model the residual structure of a
vector-autoregression analysis (VAR), also termed graphical VAR. Two network
models can then be obtained: a temporal network and a contemporaneous network.
When analyzing data from multiple subjects, a GGM can also be formed on the
covariance structure of stationary means---the between-subjects network. We
discuss the interpretation of these models and propose estimation methods to
obtain these networks, which we implement in the R packages graphicalVAR and
mlVAR. The methods are showcased in two empirical examples, and simulation
studies on these methods are included in the supplementary materials.
