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 second paper (osf.io/preprints/ps...) we conduct an extensive simulation study to evaluate whether existing software works as intended and how well multilevel HMMs can be estimated in typical time series designs in psychology.

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.

While Emmeke Aart's R-package mHMMbayes already allows estimating multilevel HMMs, there has been no easily accessible tutorial on how to use these models and no evaluation of how well these models can be estimated in situations typical for psychology research.

Thanks, but I'm afraid also this link doesn't work for me (requires a KU Leuven login)

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

... we valuate how well these models can be estimated in typical empirical settings using a simulation study ...

...we provide an introduction to LCVAR models, introduce the new R package ClusterVAR (cran.r-project.org/.../pack.../...)

Was also confused and just uploaded everything as a PDF again (: