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.

The main purpose of this package is to allow fitting of
        mixture distributions with generalised additive models for location 
        scale and shape models see Chapter 7 of Stasinopoulos et al. (2017) <doi:10.1201/b21973-4>.

This is an add on package to GAMLSS. The purpose of this
        package is to allow users to defined truncated distributions in
        GAMLSS models. The main function gen.trun() generates truncated
        version of an existing GAMLSS family distribution.

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>.

Fits generalized additive models for the location, scale and shape
    parameters of a generalized extreme value response distribution. The
    methodology is based on Rigby, R.A. and Stasinopoulos, D.M. (2005),
    <doi:10.1111/j.1467-9876.2005.00510.x> and implemented using functions from
    the 'gamlss' package <doi:10.32614/CRAN.package.gamlss>.

Estimate generalized additive mixed models via a version of
         function gamm() from 'mgcv', using 'lme4' for estimation.

Process in-situ Gamma-Ray Spectrometry for Luminescence
    Dating. This package allows to import, inspect and correct the energy
    shifts of gamma-ray spectra. It provides methods for estimating the
    gamma dose rate by the use of a calibration curve as described in
    Mercier and Falguères (2007). The package only supports Canberra CNF
    and TKA and Kromek SPE files.

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

Performs Gamma regression, where both mean and shape parameters follows lineal regression structures.

An interface for fitting generalized additive models (GAMs) and generalized additive mixed models (GAMMs) using the 'lme4' package as the computational engine, as described in Helwig (2024) <doi:10.3390/stats7010003>. Supports default and formula methods for model specification, additive and tensor product splines for capturing nonlinear effects, and automatic determination of spline type based on the class of each predictor. Includes an S3 plot method for visualizing the (nonlinear) model terms, an S3 predict method for forming predictions from a fit model, and an S3 summary method for conducting significance testing using the Bayesian interpretation of a smoothing spline.