# Estimation of extended mixed models using latent classes and latent processes: the R package lcmm

arXiv: https://arxiv.org/abs/1503.00890 · HTML: https://mlsanity.com/paper/1503.00890/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2015.

## Built on by (most cited first, 9 of 9)

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- [Clustering of longitudinal data: A tutorial on a variety of approaches](https://mlsanity.com/paper/2111.05469/) (2021)
- [Random Forests for time-fixed and time-dependent predictors: The DynForest R package](https://mlsanity.com/paper/2302.02670/) (2023)
- [Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning](https://mlsanity.com/paper/2309.15867/) (2023)
- [latrend: A Framework for Clustering Longitudinal Data](https://mlsanity.com/paper/2402.14621/) (2024)
- [A General Framework for Joint Multi-State Models](https://mlsanity.com/paper/2510.07128/) (2025)
- [Latent Neural-ODE for Model-Informed Precision Dosing: Overcoming Structural Assumptions in Pharmacokinetics](https://mlsanity.com/paper/2602.03215/) (2026)
- [An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)](https://mlsanity.com/paper/2606.05357/) (2026)
- [Neural ODE enhanced linear mixed effect models for estimating complex association patterns of time-varying covariates with the marker trajectory](https://mlsanity.com/paper/2608.29714/) (2026)

## Abstract (arXiv)

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.