At present, there are many software procedures available that enable statisticians to fit linear mixed models (LMMs) to continuous dependent variables in clustered or longitudinal datasets. LMMs are flexible tools for analyzing relationships among variables in these types of datasets, in that a variety of covariance structures can be used depending on the subject matter under study. The explicit random effects in LMMs allow analysts to make inferences about the variability between clusters or subjects in larger hypothetical populations, and examine cluster- or subject-level variables that explain portions of this variability. These models can also be used to analyze longitudinal or clustered datasets with data that are missing at random (MAR), and can accommodate time-varying covariates in longitudinal datasets. Although the software procedures currently available have many features in common, more specific analytic aspects of fitting LMMs (e.g., crossed random effects, appropriate hypothesis testing for variance components, diagnostics, incorporating sampling weights) may only be available in selected software procedures. With this article, we aim to perform a comprehensive and up-to-date comparison of the current capabilities of software procedures for fitting LMMs, and provide statisticians with a guide for selecting a software procedure appropriate for their analytic goals.

中文翻译：

---

当前拟合线性混合模型的软件程序概述

目前，有许多可用的软件程序使统计学家能够将线性混合模型 (LMM) 拟合到聚类或纵向数据集中的连续因变量。LMM 是用于分析这些类型数据集中变量之间关系的灵活工具，因为可以根据所研究的主题使用各种协方差结构。LMM 中的显式随机效应允许分析人员对较大假设人群中的集群或受试者之间的变异性进行推断，并检查解释该变异性部分的集群或受试者级变量。这些模型还可用于分析具有随机缺失 (MAR) 数据的纵向或聚类数据集，并且可以适应纵向数据集中随时间变化的协变量。尽管当前可用的软件程序具有许多共同特征，但拟合 LMM 的更具体的分析方面（例如，交叉随机效应、方差分量的适当假设检验、诊断、合并采样权重）可能仅在选定的软件程序中可用。在本文中，我们旨在对拟合 LMM 的软件程序的当前功能进行全面和最新的比较，并为统计学家提供选择适合其分析目标的软件程序的指南。