BMRMM: An R Package for Bayesian Markov (Renewal) Mixed Models

TL;DR

The BMRMM R package provides Bayesian inference tools for Markov renewal mixed models, enabling analysis of sequences with categorical states and durations influenced by exogenous factors and individual effects.

Contribution

It introduces a flexible R package implementing Bayesian Markov renewal mixed models with mixture-based transition and duration modeling, including variable selection features.

Findings

Demonstrated utility on two datasets.

Flexible modeling of state transitions and durations.

Allows variable selection for model components.

Abstract

We introduce the BMRMM package implementing Bayesian inference for a class of Markov renewal mixed models which can characterize the stochastic dynamics of a collection of sequences, each comprising alternative instances of categorical states and associated continuous duration times, while being influenced by a set of exogenous factors as well as a 'random' individual. The default setting flexibly models the state transition probabilities using mixtures of Dirichlet distributions and the duration times using mixtures of gamma kernels while also allowing variable selection for both. Modeling such data using simpler Markov mixed models also remains an option, either by ignoring the duration times altogether or by replacing them with instances of an additional category obtained by discretizing them by a user-specified unit. The option is also useful when data on duration times may not be available in the first place. We demonstrate the package's utility using two data sets.