Changes in version 1.1.3 (2022-08-18) - Released: August 2022 - Updated vignette with package logo and more detailed installation instructions - Added xlim argument to make.spaghetti() - Changed xlab default in pmf() ptmixed 1.1.2 - Released: April 2022 - Removed dependency on aod package (which is scheduled to be archived by CRAN) ptmixed 1.1.1 - Released: December 2021 - Fixed problem with NEWS file, which was not visible on CRAN any more ptmixed 1.1.0 - Released: November 2021 - Updated citation information with details of final (issued) version of the article published in Statistical Modelling in: package description, citation file, help pages and vignettes - Fixed bug in make.spaghetti() function (rows with NAs on either x or y do not cause problems any more) Changes in version 1.1 Changes in version 1.0 ptmixed 1.0.3 - Released: February 2021 - Added .checkmle() step in ptmixed() to flag as not converged problematic cases on the boundary of the parameter space ptmixed 1.0.2 - Released: January 2021 - Updated vignette - Changed displaying style for function arguments in the documentation - Corrected BibTeX formatting of author names in CITATION file - Added to make.spaghetti() code to restore bty, mar and xpd values as they were before the function call ptmixed 1.0.1 - Released: October 2020 - Updated URL in DESCRIPTION file - Added na.rm = T in computation of ylim within make.spaghetti() - Added warnings in simulate_ptglmm ptmixed 1.0.0 - Released: August 2020 - The article describing the Poisson-Tweedie GLMM is now published in Statistical Modelling! The article is published with Open Access, so anybody can freely download it from the website of Statistical Modelling - Updated package description, citation info, vignette and help pages to include references to the article - Added example on how to compute the likelihood ratio test in the vignette - Added margins and legend.space arguments to make.spaghetti(). Added automatic sorting of provided dataframe ( = no need to pre-sort it any more!) Changes in version 0.5 ptmixed 0.5.4 - Released: June 2020 - Added link to arXiv preprint in package description and vignette - Added file with citation info - Added further arguments to make.spaghetti(); cex.lab argument fixed ptmixed 0.5.2 - Released: April 2020 - Adapted code so that it runs both for balanced and unbalanced datasets (previously balanced design was assumed) - Fixed problem with visualization of vignette on CRAN page ptmixed 0.5.1 - Released: April 2020 - Added vignette to illustrate the functionalities of the package - Simplified syntax of ptmixed(), ptglm(), nbmixed() and nbglm() (wrt the id and offset arguments). ranef() function updated accordingly - Added example dataset df1, used in the ptmixed() and nbmixed() help pages. Examples in help pages revised - Added simulate_ptglmm() function, to be used for illustration purposes (in the vignettes) - Added pmf() function to visualize the pmf of a discrete variable - make.spaghetti(): fixed minor bug in that arose when the col argument was specified + added legend.inset argument Changes in version 0.4 ptmixed 0.4.3 - Released: February 2020 - Added the possibility to use Laplace approximation, which is a special case of the adaptive Gauss Hermite quadrature method where just 1 quadrature point is used (simply set npoints = 1 in ptmixed() or nbmixed()). Note: use of the Laplace is not recommended, because it is less accurate than the adaptive GH, results in lower convergence rates and can yield biased parameter estimates! We recommend using a sufficient number of quadrature points (5 typically produces a good likelihood approximation) - Added make.spaghetti() function to create a spaghetti plot / trajectory plot to visualize longitudinal data - Added example dataset df1 - Added silent argument to summary.ptglmm(). Furthermore, printed output table with parameter estimates and Wald test is now presented with at most 4 decimals - Fixed bug that caused ptglm() and nbglm()to print detailed optimization info also when trace = T ptmixed 0.4.2 - Released: January 2020 - Changed class check within wald.test() to prevent problems with future R release (4.0.0) - Fixed bug that occurred when freq.updates = 1 was set in ptmixed() and nbmixed() ptmixed 0.4.1 - Released: October 2019 - Computation of starting values for ptmixed() and nbmixed() improved - Added wald.test() function for computation of the multivariate Wald test - Added checks on maxit[1] == 0 within ptglm() and nbglm() so as to make it possible to skip BFGS optimization and go straight to Nelder-Mead - Help files revised and improved - Added extra checks in summary.ptglmm() and summary.ptglm() (to verify that the smallest eigenvalue is not too small) Changes in version 0.3 ptmixed 0.3.1 - Released: September 2019 - Added ptglm() function for the estimation of a Poisson-Tweedie GLM - Added nbmixed() and nbglm() functions for the estimation of negative binomial GLMM and GLM using the Poisson-Tweedie parametrization (negative binomial: a = 0) - The package now comprises two classes: ptglmm for objects obtained from ptmixed() and nbmixed(), and ptglm for objects obtained from ptglm() and nbglm(). Summary functions for objects of both classes have been implemented - min.var.init argument added to ptmixed() Changes in version 0.2 ptmixed 0.2.1 - Released: July 2019 - Added function to compute the empirical Bayes estimates of the random intercept - Class name of ptmixed() output changed from ptmm to ptglmm - Corrected typo in summary.ptglmm() function (the MLE of the dispersion parameter was wrongly called “deviance” instead of dispersion in the previous versions) - Added NEWS file Changes in version 0.1 ptmixed 0.1.2 - Released: June 2019 - This is a major update aimed at speeding up the maximization of the loglikelihood. When ptmixed() is called, it first attempts to maximize the loglikelihood with the Nelder-Mead algorithm and then, if this fails, with the BFGS algorithm. Until version 0.0.4 the quadrature points were updated at every iteration for both Nelder-Mead and BFGS. Starting from this version, when Nelder-Mead is called it is possible to update the positioning of the quadrature points every n iterations by setting the freq.updates argument equal to n. Default is set to freq.updates = 200 (this typically makes the optimization about 10 times faster than when freq.updates = 1) Changes in version 0.0 ptmixed 0.0.4 - Released: May 2019 - ptmixed() now outputs extra information (number of quadrature points used, initial values, warnings) - A mistake in the computation of the GH quadrature points was introduced from version 0.0.2. This has been fixed in this version ptmixed 0.0.3 - Released: May 2019 - Fixed a typo in message on initial loglikelihood value that is displayed when trace = T in ptmixed() function - Added exceptions and warnings for the case that maxit[1] and/or maxit[2] are set = 0 ptmixed 0.0.2 - Released: Apr. 2019 - Function ptmixed() does not require the specification of a time argument any more - maxit argument default value in function ptmixed() increased to c(1e4, 100) - Internal function that computes starting values improved - Added warning with indication that a simpler Poisson mixed model may fit the data sufficiently well - Added warning when initial estimate of the variance parameter is < 0.001 ptmixed 0.0.1 - Released: Feb. 2019 - First version of the package