plot_vs_normal_stable

Displays a histogram of the data with overlaid normal and alpha-stable PDFs.

Provides various functions for parameter estimation of one-dimensional
stable distributions and their mixtures. It implements a diverse set of
estimation methods, including quantile-based approaches, regression methods
based on the empirical characteristic function (empirical, kernel, and
recursive), and maximum likelihood estimation. For mixture models, it provides
stochastic expectation–maximization (SEM) algorithms and Bayesian estimation
methods using sampling and importance sampling to overcome the long burn-in
period of Markov Chain Monte Carlo (MCMC) strategies. The package also includes
tools and statistical tests for analyzing whether a dataset follows a stable
distribution. Some of the implemented methods are described in
Hajjaji, O., Manou-Abi, S. M., and Slaoui, Y. (2024) <doi:10.1080/02664763.2024.2434627>.

Solym Manou-Abi

MixStable

Parameter Estimation for Stable Distributions and Their Mixtures

Adam Najib

Yousri Slaoui

plot_vs_normal_stable function

<dl><dt>x</dt>
<dd>Numeric vector of data.</dd>
<dt>params_stable</dt>
<dd>List with alpha, beta, gamma, delta.</dd>
<dt>fig_path</dt>
<dd>Optional path to save the plot (PNG). If NULL, uses tempdir().</dd></dl>

Arguments

Plot comparison between normal and stable distributions — plot_vs_normal_stable

<dl>

<dt>x</dt>
<dd>Numeric vector of data.</dd>


<dt>params_stable</dt>
<dd>List with alpha, beta, gamma, delta.</dd>


<dt>fig_path</dt>
<dd>Optional path to save the plot (PNG). If NULL, uses tempdir().</dd>

</dl>

plot_vs_normal_stable: Plot comparison between normal and stable distributions

Description

Usage

Value

Arguments