est_r0_ml

Computes the basic reproduction number (R0) using a maximum likelihood approach
based on secondary cases and generation time weights.

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

est_r0_ml function

<dl><dt>W</dt>
<dd>Generation time distribution.</dd>
<dt>N</dt>
<dd>Secondary case counts.</dd></dl>

Arguments

Estimate R0 using maximum likelihood — est_r0_ml

<dl>

<dt>W</dt>
<dd>Generation time distribution.</dd>


<dt>N</dt>
<dd>Secondary case counts.</dd>

</dl>

est_r0_ml: Estimate R0 using maximum likelihood

Description

Usage

Value

Arguments