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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

3,157

Version

2.2.1

License

GPL (>= 2.0)

Maintainer

Cecile Proust-Lima

Last Published

February 12th, 2025

Functions in lcmm (2.2.1)

Plot of predicted trajectories and link functions

Variance-covariance of the estimates

StandardMethods

Standard methods for estimated models

Estimation of latent class linear mixed models

For internal use only ...

Update the longitudinal submodels

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

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.

Estimation of multivariate joint latent class mixed models

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

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

Predictions of the random-effects

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

Individual dynamic predictions from a joint latent class model

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

Automatic grid search

Summary of models

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

Plot of individual dynamic predictions

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

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

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

Maximum likelihood estimates

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

Plot of predicted cumulative incidences according to a profile of covariates

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

Plot of information functions

Summary of models

Plot of a fitted model

Cross classifications

Data simulation according to models from lcmm package

Wrapper to the Fortran subroutines computing the log-likelihood

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

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

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

Simulated dataset simdataHADS

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

Posterior classification and class-membership probabilities

Multivariate Wald Test

Permutation of the latent classes

Simulated dataset for hlme function

Longitudinal data on cognitive and physical aging in the elderly

Predicted cumulative incidence of event according to a profile of covariates

Simulated dataset for lcmm and Jointlcmm functions

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 (marginal) or individual data (subject specific in case of hlme).

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

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