sim_cmgnd

Simulate univariate constrained mixture of generalized normal distribution models.
Remeber to set the set.seed() before the function sim_cmgnd().

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>.

Pierdomenico Duttilo

cmgnd

Constrained Mixture of Generalized Normal Distributions

Stefano Antonio Gattone

Alfred Kume

sim_cmgnd function

<dl><dt>n</dt>
<dd>A numeric value indicating the total number of observations to simulate.</dd>
<dt>pi</dt>
<dd>A numeric vector of the mixture weights \(\pi_k\).</dd>
<dt>mu</dt>
<dd>A numeric vector of the location parameter \(\mu_k\).</dd>
<dt>sigma</dt>
<dd>A numeric vector of the scale parameter \(\sigma_k\).</dd>
<dt>nu</dt>
<dd>A numeric vector of the shape parameter \(\nu_k\).</dd></dl>

Arguments

sim_cmgnd: Function to Simulate Univariate Constrained Mixtures of Generalized Normal Distributions — sim_cmgnd

<dl>

<dt>n</dt>
<dd>A numeric value indicating the total number of observations to simulate.</dd>


<dt>pi</dt>
<dd>A numeric vector of the mixture weights \(\pi_k\).</dd>


<dt>mu</dt>
<dd>A numeric vector of the location parameter \(\mu_k\).</dd>


<dt>sigma</dt>
<dd>A numeric vector of the scale parameter \(\sigma_k\).</dd>


<dt>nu</dt>
<dd>A numeric vector of the shape parameter \(\nu_k\).</dd>

</dl>

sim_cmgnd: sim_cmgnd: Function to Simulate Univariate Constrained Mixtures of Generalized Normal Distributions

Description

Usage

Value

Arguments