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.

翻译：本文介绍了BMRMM软件包，该包为一类马尔可夫更新混合模型实现了贝叶斯推断。此类模型能够刻画一组序列的随机动态，其中每个序列包含分类状态的交替实例及其关联的连续持续时间，同时受到一组外生因素以及一个“随机”个体的影响。默认设置通过狄利克雷分布的混合灵活建模状态转移概率，并通过伽马核的混合建模持续时间，同时允许对两者进行变量选择。使用更简单的马尔可夫混合模型对此类数据进行建模同样可行，既可完全忽略持续时间，也可通过用户指定的单位将其离散化，替换为额外类别的实例。当持续时间数据本身不可用时，该选项同样有效。我们使用两个数据集展示了该软件包的实用性。