The analysis of longitudinal data poses a series of issues, but it also gives the chance to observe changes in the unit behavior over time which may be of prime interest. This has been the focus of a huge literature in the context of linear and generalized linear regression which, in the last ten years or so, has moved to the context of linear quantile regression models for continuous responses. In this paper, we present lqmix, a novel R package that helps estimate a class of linear quantile regression models for longitudinal data, in the presence of time-constant and/or time-varying, unit-specific, random coefficients, having unspecific distribution. Model parameters are estimated in a maximum likelihood framework, via an extended EM algorithm, and parameters' standard errors are estimated via a block-bootstrap procedure. The analysis of a benchmark dataset is used to give details of the package functions.

翻译：纵向数据分析带来了一系列问题，但同时也提供了观察单位行为随时间变化的契机，这可能是研究的核心关注点。这一问题在线性和广义线性回归的文献中已有大量研究，而在过去约十年间，该领域已转向针对连续响应的线性分位数回归模型。本文介绍了lqmix，一个新颖的R包，它有助于估计一类适用于纵向数据的线性分位数回归模型，这些模型可包含时不变和/或时变、单位特定、分布未指定的随机系数。模型参数通过扩展的EM算法在最大似然框架下进行估计，参数的标准误差则通过块自助法进行估计。通过对一个基准数据集的分析，详细展示了该包的函数功能。