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lcmm

Installation

To install the CRAN version, use :

install.packages("lcmm")

To get the most recent update, install it from github :

remotes::install_github("CecileProust-Lima/lcmm")

Documentation

The complete documentation is available on the lcmm website https://CecileProust-Lima.github.io/lcmm/, along with vignettes for each of the main estimating function of the package.

A detailed companion paper is also available in Journal of Statistical Software :

Proust-Lima C, Philipps V, Liquet B. Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm. Journal of Statistical Software, Articles. 2017;78(2):1-56. https://doi.org/10.18637/jss.v078.i02

And specific statistical models estimated are described in various statistical papers of the authors.

Issues

Issues and questions about the use of the lcmm package are reported on the github issue page https://github.com/CecileProust-Lima/lcmm/issues. Please check both opened and closed issues to make sure that the topic has not already been treated before creating a new issue. To report a bug, please provide a reproducible example.

We try to answer once a week and take requests into account as far as possible, but that's not our main job. Not all issues will be systematically solved.

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Version

Install

install.packages('lcmm')

Monthly Downloads

4,713

Version

2.2.2

License

GPL (>= 2.0)

Maintainer

Cécile Proust-Lima

Last Published

November 20th, 2025

Functions in lcmm (2.2.2)

Estimators of the Expected Prognostic Observed Cross-Entropy (EPOCE) for evaluating predictive accuracy of joint latent class models estimated using Jointlcmm

Automatic grid search

Estimation of a secondary regression model after the estimation of a primary latent class model

Marginal predictions of the longitudinal outcome(s) in their natural scale from lcmm, Jointlcmm or multlcmm objects

Simulated dataset for lcmm and Jointlcmm functions

Maximum likelihood estimates

Individual dynamic predictions from a joint latent class model

Predicted cumulative incidence of event according to a profile of covariates

Simulated dataset for hlme function

Estimation of latent class linear mixed models

Plot of predicted cumulative incidences according to a profile of covariates

Plot of a fitted model

Estimation of multivariate joint latent class mixed models

Wrapper to the Fortran subroutines computing the log-likelihood

Estimation of extended mixed models using latent classes and latent processes.

Estimation of mixed-effect models and latent class mixed-effect models for different types of outcomes (continuous Gaussian, continuous non-Gaussian or ordinal)

Plot of information functions

Permutation of the latent classes

Class-specific marginal or subject-specific predictions in the latent process scale for lcmm, Jointlcmm and multlcmm objects

Predictions of the random-effects

Predictions (marginal and possibly subject-specific in some cases) of a hlme, lcmm, multlcmm or Jointlcmm object in the natural scale of the longitudinal outcome(s) computed from a profile of covariates.

Posterior classification and class-membership probabilities

Marginal predictions in the natural scale of a pre-transformed outcome

Prediction of the Brownian motion or the autoregressive correlation

Simulated dataset simdataHADS

Plot of individual dynamic predictions

Plot of predicted trajectories and link functions

Estimation of multivariate mixed-effect models and multivariate latent class mixed-effect models for multivariate longitudinal outcomes of possibly multiple types (continuous Gaussian, continuous non-Gaussian/curvilinear, ordinal) that measure the same underlying latent process.

sampleParameters

Sample models parameters

Update the longitudinal submodels

Summary of models

Longitudinal data on cognitive and physical aging in the elderly

Brief summary of a hlme, lcmm, Jointlcmm,multlcmm, epoce or Diffepoce objects

Summary of a hlme, lcmm, Jointlcmm, multlcmm, mpjlcmm, externSurv, externX epoce or Diffepoce objects

Data simulation according to models from lcmm package

Cross classifications

Posterior classification stemmed from a hlme, lcmm, multlcmm or Jointlcmm estimation

Summary of models

Confidence intervals for the estimated link functions from lcmm, Jointlcmm and multlcmm

Conditional predictions of a lcmm, multlcmm or Jointlcmm object in the natural scale of the longitudinal outcome(s) for specified latent process values.

For internal use only ...

Estimates, standard errors and Wald test for the parameters of the variance-covariance matrix of the random effects.

Difference of expected prognostic cross-entropy (EPOCE) estimators and its 95% tracking interval between two joint latent class models estimated with Jointlcmm

Estimation of joint latent class models for longitudinal and time-to-event data

Conditional probabilities and item information given specified latent process values for lcmm or multlcmm object with ordinal outcomes.

Percentage of variance explained by the (latent class) linear mixed model regression

Variance-covariance of the estimates

createVarianceMatrix

Compute the variance matrix

Multivariate Wald Test

StandardMethods

Standard methods for estimated models