The R package lcmm provides a series of functions to estimate statistical
models based on linear mixed model theory. It includes the estimation of mixed
models and latent class mixed models for Gaussian longitudinal outcomes (hlme),
curvilinear and ordinal univariate longitudinal outcomes (lcmm) and curvilinear
multivariate outcomes (multlcmm), as well as joint latent class mixed models
(Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome and a
time-to-event that can be possibly left-truncated right-censored and defined in
a competing setting. Maximum likelihood esimators are obtained using a modified
Marquardt algorithm with strict convergence criteria based on the parameters
and likelihood stability, and on the negativity of the second derivatives. The
package also provides various post-fit functions including goodness-of-fit
analyses, classification, plots, predicted trajectories, individual dynamic
prediction of the event and predictive accuracy assessment. This paper
constitutes a companion paper to the package by introducing each family of
models, the estimation technique, some implementation details and giving
examples through a dataset on cognitive aging.

该研究介绍了一个名为 lcmm 的 R 包，该包提供一系列函数，能够基于线性混合模型理论来估计多种数据模型，其中包括了高斯纵向结果 (hlme)、曲线和有序单变量纵向结果 (lcmm)、曲线多变量结果 (multlcmm)、联合潜在类混合模型 (Jointlcmm) 等；该包还提供最大似然估计函数，以及可能包括其负数的二次导数基于参数和似然稳定性的严格收敛标准；此外，该包还提供多种与后拟函数有关的函数，如拟合度分析、分类、预测轨迹、个体动态预测和预测精度评估等，该文实际上是该包的同伴文件，旨在通过数据集的实际示例等方式，介绍每个数据模型的家族、估计技术和一些实施细节。