'regmhmm' Fits Hidden Markov Models with Regularization

Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488).

Hidden Markov Model (HMM) R Package Development

Introduction

This project involves the development of an R package for Hidden Markov Models (HMMs) in longitudinal data analysis. The package, named regmhmm, incorporates features for flexible modeling, variable selection using regularization techniques, and efficient computation with the Expectation-Maximization (EM) algorithm.

Features

• Flexible Modeling: Build HMMs and mixed HMMs for longitudinal data analysis.
• Variable Selection: Perform variable selection within HMMs using regularization.
• Efficient Computation: Utilize the Expectation-Maximization (EM) algorithm with coordinate descent for optimal computational performance.

Installation

# Install the development version from GitHub:
devtools::install_github("HenryLeongStat/regmhmm")