Changes in version 2.2.2 (2025-11-20) - 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 (2025-02-03) - 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 (2023-02-20) - 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 - 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 (2022-06-24) - 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 (2022-01-05) - 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 (2021-06-21) - 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 (2020-07-07) - 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 (2020-06-03) - 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 (2019-06-26) - 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 (2018-06-22) - 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 (2017-05-29) - shades in plot.predictlink/L/Y - subset in plot, which="fit" Changes in version 1.7.6 (2016-12-13) - Small bugs identified and solved in multlcmm Changes in version 1.7.5 (2016-03-16) - 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 1.7.2 (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 - 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 1.6.6 (2014-09-11) - 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 (2013-03-07) - 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 (2012-10-04) - 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 (2012-04-16) - 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=).