Changes in Version 2.2.2 (2025-11-17) :

• the functions predictY (hlme, Jointlcmm) and predictL (lcmm, multlcmm, Jointlcmm) now compute subject-specific predictions
• new function predictCor to predict the correlations (BM or AR)
• new function sampleParameters to generate the parameters of a model
• bug fixed in mpjlcmm without random effects
• bug fixed in plot(, which = “fit”) with negative observation times
• bug fixed in ItemInfo with lcmm models

Changes in Version 2.2.0 (2025-01-29) :

• new function predictYback to compute predictions in the natural scale of a pre-transformed outcome
• package spacefillr is used instead of randtoolbox
• bug fixed in externVar
• small improvement of the help pages

Changes in Version 2.1.0 (2023-10-06) :

• new function externVar to perform a secondary regression analysis after the estimation of a primary latent class model
• new argument pprior in hlme, lcmm, multlcmm and Jointlcmm to fix the probability to belong to each latent class
• packages survival, parallel, mvtnorm, randtoolbox, marqLevAlg, doParallel, numDeriv are now listed in Imports rather than in Depends
• no subject-specific predictions in multlcmm with ordinal outcomes
• corrections in mpjlcmm
• correction in predictL without random effects
• correction in epoce and predictY.Jointlcmm
• use of R’s random number generator in Fortran codes
• use double precision rather than real(kind=8) in Fortran

Changes in Version 2.0.2 (2023-02-17)

• all vignettes excepted the introduction vignette (now renamed lcmm.Rmd) are removed from the CRAN version because of too long check time.
• We now provide a website at https://CecileProust-Lima.github.io/lcmm

Changes in Version 2.0.1 (2023-02-01) :

• new vignette Joint latent class model with Jointlcmm
• new vignette Multivariate latent class model with mpjlcmm
• new argument pprior in the hlme function
• new argument computeDiscrete in the lcmm function
• mpjlcmm can be used with a mix of hlme/lcmm/multlcmm objects
• summarytable and summaryplot implement two versions of ICL criterion
• new output levels in all estimating functions
• new output varRE in hlme
• check the convergence of the initial model when using B=random()
• random parameters are generated with rmnvorm instead of using the Cholesky transformation
• permut, cuminc, VarCov, coef, vcov functions are available for mpjlcmm objects
• corrections in mpjlcmm, especially with competing risks
• correction in residuals for Jointlcmm models
• bug fixed when using posfix and partialH simultaneously
• correction in the likelihood for mutlcmm models
• bug fixed in predictClass and predictRE when using splines
• verbose=FALSE by default

Changes in Version 2.0.0 (2022-06-15) :

• the model’s estimation is now available in parallel mode!
• The optimization relies on the parallelized marqLevAlg R package.
• models with latent classes (ng>1) require initial values
• the hlme function has now a pprior argument
• the mpjlcmm function can be used without a time-to-event model
• the summary functions now shorten the parameters names
• the log-likelihood functions are now exported
• bug fixed in mpjlcmm when no random effect is included
• bug fixed in Jointlcmm with Weibull hazards and competing risks
• bug fixed in permut when used on Jointlcmm objects with competing risks
• correction of the outputs of multlcmm models

Changes in Version 1.9.4 (2022-01-03) :

• the multlcmm function is now available for ordinal outcomes (link=“thresholds”) providing a longitudinal IRT model!
• new vignette Dynamic IRT with multlcmm
• new dataset simdataHADS
• new function simulate to simulate a dataset from a hlme, lcmm, multlcmm or Jointlcmm model
• new functions ItemInfo and plot.ItemInfo to compute and plot Fisher information for ordinal outcomes
• new argument var.time in the hlme, lcmm, multlcmm and Jointlcmm functions (used in plot(, which=“fit”); issue #91)
• fix CRAN error with as.vector.data.frame
• correction in the permut function (transformation parameters were not updated)
• add envir=parent.frame() in permut and gridsearch to enable the use of these functions in a parallel setting
• fix bug in the estimation functions with infinite posterior probabilities
• the gridsearch function now checks that the initial model converged (ie minit$conv=1)
• the fixef and ranef function are now imported from the nlme package

Changes in Version 1.9.3 (2021-06-17):

• new functions predictClass, predictRE and summaryplot
• ICL computation in summaryplot
• use of rmvnorm in multlcmm to generate random initial values
• maxiter is used in the estimation of the final model in gridsearch
• fix bug in cuminc without covariates
• fix bug in the check for numeric type for argument subject with tibbles
• fix bug in predictY with hlme object when the dataset is named “x”
• fix bug in the update function when the model has unestimated parameters (posfix)
• fix bug in hlme when posterior probabilities are NA
• fix bug in plot with option which=“fit” (observations at the maximum time measurement where not systematically included)
• correction in the outputs (ppi and resid) of the mpjlcmm function

Changes in Version 1.9.2:

• event variable in joint models can be logical
• bug fixed in Jointlcmm with prior when there are missing data
• bug fixed in mpjlcmm : initial values were badly modified (with at least 3 dimensions)
• small bugs fixed in predictY with median=TRUE

Changes in Version 1.9.1:

• parallel implementation of gridsearch function. Thanks to Raphael Peter for his suggestion.
• add condRE_Y option in predictYcond
• add median options in predictY
• corrections in Jointlcmm, multlcmm and mpjlcmm when prior is specified
• bugs fixed in some prediction functions
• small bugs fixed in the summary when some parameters are not estimated
• bug fixed in VarExpl with models including BM or AR
• bug fixed in update.mpjlcmm (variance matrix was not correct)
• manage infinite ppi in hlme
• correction of epsY type, URL in vignettes, data statements position

Changes in Version 1.8.1:

• new function mpjlcmm for estimating joint latent class models with multiple markers and/or latent processes
• various post-fit functions for mpjlcmm objects
• new functions permut and xclass
• creation of vignettes, thanks to Samy Youbi for his help
• variable subject must be numeric
• in plot(which=‘fit’), time intervals do not depend on subset
• add score test result in summarytable
• bug fixed in lcmm with prior
• bug fixed in Jointlcmm with infinite score test
• bug fixed in dynpred with TimeDepVar

Changes in Version 1.7.9:

• bug in summary when the model did not converge
• bug in dynpred when draws=TRUE and only 1 horizon or 1 landmark, or when o covariates are included in the survival model, or when using factor
• bug in Jointlcmm when using B=m1
• bug in plot.predictY with CI
• bug in Jointlcmm when B=random(m1)

Changes in Version 1.7.8:

• shades in plot.predictlink/L/Y
• subset in plot, which=“fit”

Changes in Version 1.7.6 (2016-12-12):

• Small bugs identified and solved in multlcmm

Changes in Version 1.7.5 (2016-03-15):

• Small bugs identified and solved in multlcmm, predictY and predictL

Changes in Version 1.7.4 (2015-12-26):

• The package uses lazydata to automatically load the datasets of the package.

• jlcmm and mlcmm are shortcuts for functions Jointlcmm and multlcmm, respectively.

• Function gridsearch provides an automatic grid of departures for reducing the odds of converging towards a local maximum.

• Initial values can be randomly generated from a model with 1 class (called m1 in next example) with option B=random(m1) in hlme, lcmm, multlcmm and Jointlcmm.

Changes in Version 1.7.3.0 (2015-10-23):

• Functions hlme, lcmm, multlcmm, Jointlcmm now include a posfix option to specify parameters that should not be estimated.

• Functions lcmm, multlcmm, Jointlcmm now include a partialH option to restrict the computation of the inverse of the Hessian matrix to a submatrix

• Functions hlme, lcmm, multlcmm, Jointlcmm now allow optional vector B to be an estimated model (with G=1) to reduce calculation time of initial values.

• Bug identified and solved in calculation of subject-specific predictions in hlme, lcmm, multlcmm and Jointlcmm when cor is not NULL.

• Bug identified and solved in the calculation of confidence bands for individual dynamic predictions in dynpred with draws=T.

• Bug identified and solved in the calculation of the explained variance for multlcmm objects when cor is not NULL.

Changes in Version 1.7.1 & 1.7.2 (2015-02-27):

• Function plot now includes a which=“fit” option to plot observed and predicted trajectories stemming from a hlme, lcmm, Jointlcmm or multlcmm object.

• Function predictlink replaces deprecated function link.confint

• Function plot gathers deprecated functions plot.linkfunction, plot.baselinerisk, plot.survival, plot.fit together

Changes in Version 1.7.0 (2015-02-13):

• The function Jointlcmm now allows competing risks data for the survival part and is also available for non-Gaussian longitudinal data. All existing methods for Jointlcmm objects (except EPOCE and Diffepoce functions) are adapted to the new framework.

• Functions link.confint, plot.linkfunction, predictL are now available for Jointlcmm objects.

• The new functions incidcum and plot.incidcum respectively compute and plot the cumulative incidence associated to each competing event for Jointlcmm object.

• The new function fitY computes the marginal predicted values of longitudinal outcomes in their natural scale for lcmm or multlcmm objects.

• Bug identified and solved in dynpred function when used with a joint model assuming proportional hazards between latent classes.

• The Makevars file now allows compilation of the package with parallel make.

Changes in Version 1.6.5 & 1.6.6 (2014-09-10):

• bug solved regarding installation problem with parallel make

Changes in Version 1.6.4 (2014-04-11):

• The new functions dynpred and plot.dynpred respectively compute and plot individual dynamic predictions obtained from a joint latent class model estimated by Jointlcmm.

• The new function VarCovRE computes the standard errors of the parameters of variance-covariance of the random effects for a hlme, lcmm, Jointlcmm or multlcmm object

• The new function WaldMult computes multivariate Wald tests and Wald tests for combinations of parameters from hlme, lcmm, Jointlcmm or multlcmm object

• The new function VarExpl computes the percentages of variance explained by the linear regression for a hlme, lcmm, Jointlclmm or multlcmm object

• The new functions estimates and VarCov get respectively all parameters estimated and their variance-covariance matrix for a hlme, lcmm, Jointlcmm or multlcmm object

• Function summary now returns the table containing the results about the fixed effects in the longitudinal model

• All plots consider now the … options

• Functions plot.linkfunction and plot.predict have now an add argument

• Function multlcmm now allows “splines” or “Splines” specification for the link functions

• Functions lcmm and multlcmm now compute the transformations even if the maximum number of iterations is reached without convergence

• bug identified and solved in multlcmm when the response variables are not integers

• bug identified and solved in multlcmm when using contrast

• bug identified and solved in plot.linkfunction for the y axes positions

• bug identified and solved in hlme, lcmm, Jointlcmm and multlcmm when including interactions in mixture.

Changes in Version 1.6.2 (2013-03-06):

• The new function multlcmm now estimates latent process mixed models for multivariate curvilinear longitudinal outcomes (with link functions: linear, beta or splines). Various post-fit computation and output functions are also available including plot.linkfunction, predictY, predictL, etc

• All the functions hlme, lcmm, Jointlcmm include a cor option for including a brownian motion or a first-order autoregressive error process in addition to the independent errors of measurement

• bug identified and solved in predictL, predictY and plot.predict when used with factor covariate

Changes in Version 1.5.8 (2012-10-01):

• bug identified and solved in predictY.lcmm when used with a splines link function and an outcome with minimum value not at 0

Changes in Version 1.5.7 (2012-07-24):

• The function predictY now computes the predicted values (possibly class-specific) of the longitudinal outcome not only from a lcmm object but also from a hlme or a Jointlcmm object for a specified profile of covariates.

• bug identified and solved in predictY.lcmm when used with a threshold link function and a Monte Carlo method

Changes in Version 1.5.6 (2012-07-16):

• missing data handled in hlme, lcmm and Jointlcmm using na.action with attributes 1 for na.omit or 2 for na.fail

• The new function predictY.lcmm computes predicted values of a lcmm object in the natural outcome scale for a specified profile of covariates, and also provides confidence bands using a Monte Carlo method.

• bugs in epoce computation solved (with splines baseline risk function, and/or NaN values under solaris system)

• bug identified and solved in summary functions regarding the labels of covariate effects in peculiar cases

Changes in Version 1.5.2 (2012-04-06):

• improved variable specification in the estimating functions Jointlcmm, lcmm and hlme with

  • categorical variables using factor()
  • variables entered as functions using I()
  • interaction terms using “*” and “:”

• computation of the predictive accuracy measure EPOCE from a Jointlcmm object either on the training data or on external data (post-fit functions epoce and Diffepoce)

• for discrete outcomes, lcmm function now computates the posterior discrete log-likelihood and the universal approximate cross-validation criterion (UACV)

• Jointlcmm now includes two parameterizations of I-splines and piecewise-constant baseline risks functions to ensure positive risks: either log/exp or sqrt/square (option logscale=).