Unsupervised Model Construction in Continuous-Time.

Many of the advancements reconciling individual- and group-level results have occurred in the context of a discrete-time modeling framework. Discrete-time models are intuitive and offer relatively simple interpretations for the resulting dynamic structures; however, they do not possess the flexibility of models fitted in the continuous-time framework. We introduce ct-gimme, a continuous-time extension of the group iterative multiple model estimation (GIMME; Gates & Molenaar, 2012) procedure which enables researchers to fit complex, high dimensional dynamic networks in continuous-time. Our results indicate that ct-gimme outperforms $N=1$ model fitting in continuous-time by pooling information across multiple subjects. Likewise, ct-gimme outperforms group-level model fitting in the presence of within-sample heterogeneity. We conclude with an empirical illustration and highlight limitations of the approach relating to identification of meaningful starting values.