export_analysis_report

Saves the results of a stability analysis to a JSON file and an Excel workbook.

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

export_analysis_report function

<dl><dt>data</dt>
<dd>Numeric vector of data.</dd>
<dt>stable_params</dt>
<dd>List of estimated stable parameters.</dd>
<dt>qcv</dt>
<dd>QCV statistic.</dd>
<dt>skew_kurt</dt>
<dd>List with skewness and kurtosis.</dd>
<dt>normality</dt>
<dd>List of normality test results.</dd>
<dt>verdict</dt>
<dd>Final verdict string.</dd>
<dt>filename</dt>
<dd>Base name for output files.</dd></dl>

Arguments

Export analysis report to JSON and Excel — export_analysis_report

<dl>

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


<dt>stable_params</dt>
<dd>List of estimated stable parameters.</dd>


<dt>qcv</dt>
<dd>QCV statistic.</dd>


<dt>skew_kurt</dt>
<dd>List with skewness and kurtosis.</dd>


<dt>normality</dt>
<dd>List of normality test results.</dd>


<dt>verdict</dt>
<dd>Final verdict string.</dd>


<dt>filename</dt>
<dd>Base name for output files.</dd>

</dl>

export_analysis_report: Export analysis report to JSON and Excel

Description

Usage

Value

Arguments