run_estimations_with_gibbs

Executes EM algorithms with Gibbs sampling on the input data.
Optionally saves mixture plots to a temporary directory.

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

run_estimations_with_gibbs function

<dl><dt>data</dt>
<dd>Numeric vector of observations.</dd>
<dt>bw_sj</dt>
<dd>Numeric. Bandwidth value (e.g., Silverman's rule).</dd>
<dt>max_iter</dt>
<dd>Integer. Maximum number of EM iterations.</dd>
<dt>tol</dt>
<dd>Numeric. Convergence tolerance.</dd>
<dt>save_plots</dt>
<dd>Logical. Whether to save PNG plots to tempdir().</dd></dl>

Arguments

Run all EM-based estimations with Gibbs sampling (CRAN-safe) — run_estimations_with_gibbs

<dl>

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


<dt>bw_sj</dt>
<dd>Numeric. Bandwidth value (e.g., Silverman's rule).</dd>


<dt>max_iter</dt>
<dd>Integer. Maximum number of EM iterations.</dd>


<dt>tol</dt>
<dd>Numeric. Convergence tolerance.</dd>


<dt>save_plots</dt>
<dd>Logical. Whether to save PNG plots to tempdir().</dd>

</dl>

run_estimations_with_gibbs: Run all EM-based estimations with Gibbs sampling (CRAN-safe)

Description

Usage

Value

Arguments