validate_params

Ensures parameters are within valid bounds for stable distribution computations.

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

validate_params function

<dl><dt>alpha</dt>
<dd>Stability parameter (must be &gt; 0 and &lt; 2).</dd>
<dt>beta</dt>
<dd>Skewness parameter (clipped to [-1, 1]).</dd>
<dt>delta</dt>
<dd>Scale parameter (must be &gt; 0).</dd>
<dt>r</dt>
<dd>Optional numeric vector. Must be positive if provided.</dd></dl>

Arguments

Validate and clip parameters for stable distribution — validate_params

<dl>

<dt>alpha</dt>
<dd>Stability parameter (must be &gt; 0 and &lt; 2).</dd>


<dt>beta</dt>
<dd>Skewness parameter (clipped to [-1, 1]).</dd>


<dt>delta</dt>
<dd>Scale parameter (must be &gt; 0).</dd>


<dt>r</dt>
<dd>Optional numeric vector. Must be positive if provided.</dd>

</dl>

validate_params: Validate and clip parameters for stable distribution

Description

Usage

Value

Arguments