1-4 September 2026

The course “Markov and hidden Markov models with applications in R” introduces the basic theoretical foundations of the Markov and hidden (or latent) Markov models, with a focus on applications to longitudinal data. Participants will learn how to specify, estimate and interpret these models, including extensions with covariates and applications to continuous, categorical and incomplete longitudinal responses. The course combines methodological explanations and inferential procedures with practical exercises in R using the LMest library. Interactive practice sessions will allow participants to discuss concrete use cases and applied research questions. 
The course is intended for students from academia and for practitioners from industry and consultancy with little or no background in probability who wish to apply Markov and hidden Markov models. Basic familiarity with R is desirable but not mandatory.

The course will be based on some chapters of the book Latent Markov models for Longitudinal Data (Bartolucci, Farcomeni, and Pennoni, 2013). Empirical illustrations and exercises will be carried out using the R library LMest (Pennoni, Pandolfi, Bartolucci, 2025).

Bartolucci, F., Farcomeni, A., and Pennoni, F. (2013). Latent Markov models for longitudinal data. CRC Press, Boca Raton.
Pennoni, F., Pandolfi, S., and Bartolucci, F. (2025). LMest: An R Package for Estimating Generalized Latent Markov Models. The R Journal, 16, 74-101.

After completing this course, participants will:

1.    Be familiar with the basics of discrete latent variable models.
2.    Be able to estimate different Markov and hidden Markov models with continuous and categorical longitudinal data, including cases with missing values.
3.    Know how to perform model selection and estimate the uncertainty of model parameters using bootstrap procedures.

Tuesday 1 September – 14:00-17:00 CEST Berlin time
Course overview, practicalities. Introduction to latent variable models with a focus on discrete latent variable models. Basic features of the finite mixture and Markov model with reference to the estimation method and to the Expectation-Maximization (EM) algorithm. Applications with the R library using RMarkdown.

 Wednesday 2 September - 14:00-17:00 CEST Berlin time
Introduction to longitudinal data. Basic features of the Hidden Markov model for continuous and categorical longitudinal data with and without covariates, estimation and model selection procedures. Applications and exercises with the R library LMest using RMarkdown.

 Thursday 3 September - 14:00-17:00 CEST Berlin time
Hidden Markov model formulations for multivariate data, covariates and more complex data structures. Recent case studies and applications. Exercises with the R library LMest.

 Friday 4 September- 14:00-17:00 CEST Berlin time
1. Good practices: how to report results, corrections of the assigned exercises, …
2. [topics of interest to the audience / general discussion]

"I enjoyed the course a lot. I had some background with latent class models and Markov models, but was unfamiliar with a lot of the content on Hidden Markov models. This can be directly applied to some of the work I am doing."

"Very informative course with a good mix of lectures and hands-on sessions"

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2- Network Analysis in System Biology with R/Bioconductor - ONLINE, 5-8 October

3 - Bayesian Methods for Biomedical Data Science - ONLINE, 30 November - 4 December