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Category: Blog

Dear GAMLSS friends and users Our previous website `www.gamlss.org’  hosted at Hostgator was hacked, so  we took the decision  to move our site to a new host and restart the web site under the old  `www.gamlss.com’  name.  We currently redirect all  `www.gamlss.org’ traffic to `www.gamlss.com’. On moving the site we lost some material. We apologise […]

 1. package: gamlss i) The glim.fit() function within gamlss() has a line added to prevent the iterative weighs wt to go to Inf. ii) The tp() function within lms() and quantSheets() has changed name and modified slightly iii) The vcoc.gamlss() has the warnings changed and allows if theinverse of the Hessian (R) fails to recalucated […]

Version 4.2-7 i) gamlss gamlssML(): now allows the fitting binomial data (sorry it never checked before) and the use of formula in the specification of the model (e.g, y~1) to be consistent with gamlss(). Note that explanatory variables will be ignored if used with gamlssML().  .gamlss.multin.list is now on NAMESPACE  the functions vcov.gamlss() and summary.gamlss() […]

This version is released on the 22–6-2013 and it is the first time that robust (sandwich) standard errors are introduce  in gamlss models.  Of course those standard errors apply to parametric GAMLSS models only. When non-parametric smoothing terms are used then  the (sandwich) standard errors can still be used with caution since they are not yet take […]

  Version 4.2-5 The most important change in this version of gamlss is the way that the standard errors are calculated. In  previous version the vcov() function was calculated using a final iteration to a non-linear maximisation procedure. This procedure failed in a lot of occasions and the result was that the reported standard errors […]

The new version of gamlss is 4.2-0. The following are the changes made:   package gamlss: The functions prof.dev() and prof.term() are improved. The argument step is not anymore compulsory and if not set the argument length is used instead. For most cases there is no need to have a fine grid since the function is approximated using splinefun(). The output is […]

The new features in version 2.1-2 are as follows: package gamlss: The function histSmo() is added for density estimation. The function histDist() now has the function gamlssML() as its main fitting function. The fitting function  gamlss() is only used if gamlssML() fails. The function gamlssML() has now an argument start.from. In the function fitDist(), the normal distribution NO() is added to the list of “.realline” so it also appears […]