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Posts by Author: 19 posts by stasinopoulos

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 […]

  Centile estimation includes methods for estimating the age related distribution of human growth. The standard estimation of centile curves involves two continuous variables: the response variable, that is, the variable we are interested in and for which we are trying to find the centile curves, e.g. weight, BMI, head circumference etc. and the explanatory variable age. The […]

  Books   One  book on GAMLSS is published and two others are  in preparation  `Flexible Regression and Smoothing:  Using  GAMLSS  in R’   was published in April 2017. See  here for an old draft version. `Distributions for Location Scale and Shape: Using  GAMLSS  in R’, is in preparation. See  here for the November 2017  draft […]

Additive terms in the gamlss package In the GAMLSS implementation in R, the function gamlss() allows modelling all the distribution parameters μ, σ, ν and τ as linear and/or non-linear and/or ‘non- parametric’ smoothing functions of the explanatory variables. This allow the explanatory variables to effect the predictors, (the η’s), of the specific parameters and therefore […]

  * the original gamlss package for fitting GAMLSS (now depends on gamlss.dist and gamlss.data * the gamlss.add package for extra additive terms. * the gamlss.boot experimental package for bootstrapping centile curves (not in CRAN). * the gamlss.cens package for fitting censored (interval) response variables. * the gamlss.data package for all example data used in GAMLSS. * […]

  The R package gamlss.dist contains more than 70 distributions. We are refer to those distribution as “gamlss.family” distribution a name also given to the equivalent R objects. Each of those “gamlss.family” distributions has five related functions: the probability density function (d) the cumulative distribution function (p) the inverse of the cumulative distribution function and […]