cmgnd
Constrained Mixture of Generalized Normal Distributions
The 'cmgnd' implements the constrained mixture of generalized normal distributions model, a flexible statistical framework for modelling univariate data exhibiting non-normal features such as skewness, multi-modality, and heavy tails. By imposing constraints on model parameters, the 'cmgnd' reduces estimation complexity while maintaining high descriptive power, offering an efficient solution in the presence of distributional irregularities. For more details see Duttilo and Gattone (2025)
cmgnd 
Installation
You can install the development version of cmgnd from GitHub with:
# install.packages("devtools")
devtools::install_github("pierdutt/cmgnd")
cmgnd is an R package designed to analyse univariate data with complex patterns, including asymmetry, multi-modality, and heavy tails. The package implements the univariate constrained mixture of generalized normal distributions (cmgnd) model, allowing parameter constraints to be applied globally or to specific subpartitions of mixture components, thereby reducing model complexity and improving estimation performance.
References
Duttilo, P. (2024). Modelling financial returns with mixtures of generalized normal distributions. PhD Thesis, University “G. d’Annunzio” of Chieti-Pescara, pp. 1-166, arXiv:2411.11847
Duttilo, P. and Gattone, S.A. (2025). Enhancing parameter estimation in finite mixture of generalized normal distributions, Computational Statistics, pp. 1-28, 10.1007/s00180-025-01638-x
Duttilo, P., Gattone, S.A., and Kume A. (2025). Constrained mixtures of generalized normal distributions, pp. 1-34, arXiv:2506.03285