A modern approach to transition analysis and process mining with Markov models: A tutorial with R

Abstract This chapter presents an introduction to Markovian modeling for the analysis of sequence data. Contrary to the deterministic approach seen in the previous sequence analysis chapters, Markovian models are probabilistic models, focusing on the transitions between states instead of studying sequences as a whole. The chapter provides an introduction to this method and differentiates between its most common variations: first-order Markov models, hidden Markov models, mixture Markov models, and mixture hidden Markov models.

Clustering and Structural Robustness in Causal Diagrams

Abstract Graphs are commonly used to represent and visualize causal relations. For a small number of variables, this approach provides a succinct and clear view of the scenario at hand. As the number of variables under study increases, the graphical approach may become impractical, and the clarity of the representation is lost. Clustering of variables is a natural way to reduce the size of the causal diagram, but it may erroneously change the essential properties of the causal relations if implemented arbitrarily.

Price Optimization Combining Conjoint Data and Purchase History: A Causal Modeling Approach

Abstract Pricing decisions of companies require an understanding of the causal effect of a price change on the demand. When real-life pricing experiments are infeasible, data-driven decision-making must be based on alternative data sources such as purchase history (sales data) and conjoint studies where a group of customers is asked to make imaginary purchases in an artificial setup. We present an approach for price optimization that combines population statistics, purchase history and conjoint data in a systematic way.

From sequences to variables - Rethinking the relationship between sequences and outcomes

Abstract Sequence analysis (SA) has gained increasing interest in social sciences for the holistic analysis of life course and other longitudinal data. The usual approach is to construct sequences, calculate dissimilarities, group similar sequences with cluster analysis, and use cluster membership as a dependent or independent variable in a linear or nonlinear regression model. This approach may be problematic as the cluster memberships are assumed to be fixed known characteristics of the subjects in subsequent analysis.

dynamite: An R Package for Dynamic Multivariate Panel Models

Abstract dynamite is an R package for Bayesian inference of intensive panel (time series) data comprising of multiple measurements per multiple individuals measured in time. The package supports joint modeling of multiple response variables, time-varying and time-invariant effects, a wide range of discrete and continuous distributions, group-specific random effects, latent factors, and customization of prior distributions of the model parameters. Models in the package are defined via a user-friendly formula interface, and estimation of the posterior distribution of the model parameters takes advantage of state-of-the-art Markov chain Monte Carlo methods.