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by M Stasinopoulos · 2022 · Cited by 9 — Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape. (GAMLSS) in R. Journal of Statistical Software, by X Barber · 2018 · Cited by 3 — The book is divided into six parts: Introduction to models and packages, Algorithms, Func- tions and inference, Distributions, Additive terms, This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear by M Stasinopoulos · 2022 · Cited by 12 — Non-parametric pdf from limited information Those functions are used in the distribution book of gamlss, see Rigby et. al 2019. 5 Jul 2022 — PDF | GAMLSS is a general framework for fitting regression type models where the distribution of the response variable does not have to This book is designed for a short course in GAMLSS given at the University of Athens. The gamlss manual, Instructions on how to use the gamlss package. by M Stasinopoulos · 2008 · Cited by 5 — This document contains a brief introduction to Generalized Additive Models for Location, Scale and Shape (GAMLSS)1 and information on how to install and use by RA Rigby · 2019 · Cited by 183 — This book follows the earlier book 'Flexible Regression and Smoothing: Using GAMLSS in R', [Stasinopoulos et al., 2017], which focused on the5 Jul 2022 — PDF | This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS).