Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Francesco Bartolucci, Silvia Pandolfi, Fulvia Pennoni
Title: LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data
Abstract: Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data, especially when response variables are categorical. These models have a great potential of application in many fields, such as economics and medicine. We illustrate the R package LMest that is tailored to deal with the basic LM model and some extended formulations accounting for individual covariates and for the presence of unobserved clusters of units having the same initial and transition probabilities (mixed LM model). The main functions of the package are tailored to parameter estimation through the expectation-maximization algorithm, which is based on suitable forwardbackward recursions. The package also permits local and global decoding and to obtain standard errors for the parameter estimates. We illustrate the use of the package and its main features through some empirical examples in the fields of labour market, health, and criminology.

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Paper: LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data     Download PDF (Downloads: 5309)
LMest_2.4.1.tar.gz: R source package Download (Downloads: 218; 169KB)
v81i04.R: R replication code Download (Downloads: 297; 4KB)

DOI: 10.18637/jss.v081.i04

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.