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gamlss.inf (version 1.0-2)

Fitting Mixed (Inflated and Adjusted) Distributions

Description

This is an add-on package to 'gamlss'. The purpose of this package is to allow users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape) models when the response variable is defined either in the intervals [0,1), (0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive real line including zero (zero-adjusted distributions). The mass points at zero and/or one are treated as extra parameters with the possibility to include a linear predictor for both. The package also allows transformed or truncated distributions from the GAMLSS family to be used for the continuous part of the distribution. Standard methods and GAMLSS diagnostics can be used with the resulting fitted object.

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Version

Install

install.packages('gamlss.inf')

Monthly Downloads

2,958

Version

1.0-2

License

GPL-2 | GPL-3

Maintainer

Marco ENEA

Last Published

April 14th, 2025

Functions in gamlss.inf (1.0-2)

term.plotInf0to1

Plot regression terms for a specified parameter of a fitted gamlssInf0to1 object
term.plotZadj

Plot regression terms for a specified parameter of a fitted gamlssZadj object
gen.Inf0to1

Functions to generate inflated 0-to-1 distributions from existing continuous gamlss.family distributions defined in (0,1).
centiles.Inf0to1

Plotting centile curves for a gamlssInf0to1 and gamlssZadj object
gamlssZadj

Fitting positive real line response variable with zeros.
summary.gamlssinf0to1

Summarizes an inflated GAMLSS fitted model
gamlss.inf-package

Models for Mixed (Inflated and Adjusted) Response Variables.
predict.gamlssZadj

Extract Predictor Values and Standard Errors For New Data in a gamlssZadj Model
gen.Zadj

Functions to generate zero adjusted distributions from existing continuous gamlss.family distributions defined on positve real line.
gamlssInf0to1

GAMLSS model for a proportion response variable with point(s) mass at 0 and or 1.
sda

Data for using for simulation
predict.gamlssinf0to1

Extract Predictor Values and Standard Errors For New Data In a gamlssinf0to1 Model