We found that model selection was successful for state separations similar to the ones we found in empirical data and estimation error was satisfactory for realistic numbers of observations and subjects.

Going beyond existing simulations, we independently varied the number of true states, the state separation, the number of variables, the number of time points, and the number of subjects.

In the first paper with Emmeke Aarts (osf.io/preprints/ps...) we provide a gentle introduction to (multilevel) HMMs and walk the reader through a fully reproducible tutorial on model specification, estimation, selection, and interpretation by analyzing a typical EMA emotion time series dataset.

Two new preprints on multilevel HMMs! Time series data is now pervasive in psychology and new methods are needed to model the dynamics in such data. Hidden Markov Models (HHMs) are powerful models for dynamics in which a system is switching between a number of discrete states.

... and provide a fully reproducible step-by-step tutorial on model estimation, selection, diagnostics and interpretation.

New preprint by Anja Ernst and me on Modeling Qualitative Between-Person Heterogeneity in Time-Series using Latent Class Vector Autoregressive Models (osf.io/preprints/ps...)

Drawing on existing (process) theories of emotion, we outline how our model can be extended to render it adaptive, include learning, and include inter-individual differences 3/4

Despite the fact that our model is extremely simple and clearly incomplete, it already reproduces nine statistical phenomena which have been established in the emotion time series literature.
2/4

Emotions are reactions to situations we encounter in daily life. In our new paper in Psych Review (psycnet.apa.org/fulltext/202...; with @oisinryan.bsky.social and @fdabl.bsky.social), we take a first step towards building a generative model for emotion dynamics based on this simple principle 1/4

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