Density, distribution function, quantile function and random generation for the bimodal skew symmetric normal distribution of Hassan and El-Bassiouni (2016) <doi:10.1080/03610926.2014.882950>.

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.

Demos for smoothing and gamlss.family distributions.

This package provides data used as examples to demonstrate GAMLSS models.

This package contains infrastructure for using mboost::gamboost() in order to estimate multistate models.

Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.

This is an add-on package to GAMLSS. The purpose of this package is to allow users to fit interval response variables in GAMLSS models. The main function gen.cens() generates a censored version of an existing GAMLSS family distribution.

This package provides a set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a log or a logit transformation, respectively.

Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2024) <doi:10.1007/s10182-023-00490-y>.

Interface for extra high-dimensional smooth functions for Generalized Additive Models for Location Scale and Shape (GAMLSS) including (adaptive) lasso, ridge, elastic net and least angle regression.

Read, analyze, modify, and write GAMS (General Algebraic Modeling System) data. The main focus of gamstransfer is the highly efficient transfer of data with GAMS <https://www.gams.com/>, while keeping these operations as simple as possible for the user. The transfer of data usually takes place via an intermediate GDX (GAMS Data Exchange) file. Additionally, gamstransfer provides utility functions to get an overview of GAMS data and to check its validity.

Identifies biomarkers that exhibit differential response dynamics by time across groups and estimates kinetic properties of biomarkers.

Computational intensive calculations for Generalized Additive Models for Location Scale and Shape, <doi:10.1111/j.1467-9876.2005.00510.x>.

This package provides functions for plotting Generalized Additive Models for Location Scale and Shape from the gamlss package, Stasinopoulos and Rigby (2007) <doi:10.18637/jss.v023.i07>, using the graphical methods from ggplot2'.

Simulates a gambling game under the gambler's ruin setup, after asking for the money you have and the money you want to win, along with your win probability in each round of the game.