News about R package lcmm :
---------------------------




Changes in Version 1.6.3 (2013-03-14):

* bug identified and solved in multlcmm when only one covariate was included in the fixed part.

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=).