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Description

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) <doi:10.1007/s00180-025-01638-x> and Duttilo et al (2025) <doi:10.48550/arXiv.2506.03285>.

cmgnd logo

CRANstatus Lifecycle:experimental develversion License:GPL-3

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

Metadata

Version

0.1.1

License

Unknown

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