Work

lqmix: an R Package for Longitudinal Data Analysis via Linear Quantile Mixtures

Year: 2025

Type: article

Abstract: The analysis of longitudinal data gives the chance to observe how units' behavior changes over time, but it also poses a series of issues. These have been the focus of an extensive literature in the context of linear and generalized linear regression, moving also, in the last ten years or so, to the context of linear quantile regression for continuous responses. In this paper, we present `lqmix`, a novel `R` package that assists in estimating a class of linear quantile regression models for longitudinal data, in the presence of time-constant and/or time-varying, unit-specific, random coefficients, with unspecified distribution. Model parameters are estimated in a maximum likelihood framework via an extended EM algorithm, while the corresponding standard errors are derived via a block-bootstrap procedure. The analysis of a benchmark dataset is used to give details on the package functions.

Source: The R Journal

DOI: 10.32614/rj-2025-029

Authors: Marco Alfó, Maria Francesca Marino, Maria Giovanna Ranalli, Nicola Salvati

Institutions: Sapienza University of Rome, University of Florence, University of Perugia, University of Pisa

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: Ministero dell’Istruzione, dell’Università e della Ricerca, Università degli Studi di Perugia

Awards: G1234657840, G1575657597, G6441287156, G8140762014