GAMLSS Modeling with Advanced Flexible Infrastructures
Description
Details
The primary purpose of this package is to facilitate the creation of advanced infrastructures designed to enhance the Generalized Additive Models for Location Scale and Shape (GAMLSS, Rigby and Stasinopoulos 2005) modeling framework. Notably, the gamlss2 package represents a significant overhaul of its predecessor, gamlss, with a key emphasis on improving estimation speed and incorporating more adaptable infrastructures. These enhancements enable the seamless integration of various algorithms into GAMLSS, including gradient boosting, Bayesian estimation, regression trees, and forests, fostering a more versatile and powerful modeling environment.
Moreover, the package expands its compatibility by supporting all model terms from the base R mgcv package. Additionally, the gamlss2 package introduces the capability to accommodate more than four parameter families. Essentially, this means that users can now specify any type of model using these new infrastructures, making the package highly flexible and accommodating to a wide range of modeling requirements.
References
Rigby RA, Stasinopoulos DM (2005). “Generalized Additive Models for Location, Scale and Shape (with Discussion).” Journal of the Royal Statistical Society, Series C (Applied Statistics), 54, 507–554. doi:10.1111/j.1467-9876.2005.00510.x
Rigby RA, Stasinopoulos DM, Heller GZ, De Bastiani F (2019). Distributions for Modeling Location, Scale, and Shape: Using GAMLSS in R, Chapman and Hall/CRC. doi:10.1201/9780429298547
Stasinopoulos DM, Rigby RA (2007). “Generalized Additive Models for Location Scale and Shape (GAMLSS) in R.” Journal of Statistical Software, 23(7), 1–46. doi:10.18637/jss.v023.i07
Stasinopoulos DM, Rigby RA, Heller GZ, Voudouris V, De Bastiani F (2017). Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC. doi:10.1201/b21973
See Also
gamlss2, fake_formula