foxp2sm

<p>A shortened version of the <code>foxp2</code> data set for demonstrating R examples.
See details of the <code>foxp2</code> data set by calling ?foxp2.</p>

datasets

The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) <doi:10.1080/01621459.2018.1423986> and Yutong Wu et al., (2022) <doi:10.1093/biostatistics/kxac050>.

Yutong Wu

BMRMM

An Implementation of the Bayesian Markov (Renewal) Mixed Models

Abhra Sarkar

foxp2sm function

<p>An object of class <code>data.frame</code> with 69 rows and 6 columns.</p>

foxp2sm: Shortened Simulated FoxP2 Data Set.

Description

Usage

Arguments

Format