News about R package lcmm : --------------------------- A detailed companion paper is 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. 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=).