Our lab focuses on quantitative aspects of modeling psychological and behavioral data both in terms of analysis methods and study design. Of high interest are methods that can address within- and between person differences across multiple time scales and different measurement frequencies. The focus is always on methods that can navigate the multivariate nature of human behavior. One of our current projects focuses on Bayesian approaches to mixed effects location scale models for high frequency longitudinal data. These models are useful, for example, to investigate the relation among longer term changes in health outcomes and short-term fluctuations in daily affect within persons. Moreover, our focus is on multivariate models and on Bayesian estimation techniques for low- and high dimensional settings that are common in network models. To address our research questions we rely mostly on computational methods and simulations and on programs such as Stan, Jags, R, and Julia.