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Category: Portfolio

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 […]

  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 […]

  The diagnostics for GAMLSS models are based on the residuals of the fitted model.The GAMLSS models use the  normalised quantile residuals for continuous response variables and randomised normalised quantile residuals for discrete response variables. The main advantage of the normalised (randomised) quantile residuals is that, whatever the distribution of the response variable their true values […]