sim_epi_data

Perform change points detection on univariate and multivariate time series according to the methods presented by Asael Fabian Martínez and Ramsés H. Mena (2014) <doi:10.1214/14-BA878> and Corradin, Danese and Ongaro (2022) <doi:10.1016/j.ijar.2021.12.019>. It also clusters different types of time dependent data with common change points, see "Model-based clustering of time-dependent observations with common structural changes" (Corradin,Danese,KhudaBukhsh and Ongaro, 2024) <doi:10.48550/arXiv.2410.09552> for details.

Luca Danese

BayesChange

Bayesian Methods for Change Points Analysis

Riccardo Corradin

Andrea Ongaro

sim_epi_data function

<dl><dt>S0</dt>
<dd>number of individuals in the population.</dd>
<dt>I0</dt>
<dd>number of infected individuals at time 0.</dd>
<dt>max_time</dt>
<dd>maximum observed time.</dd>
<dt>beta_vec</dt>
<dd>vector with the infection rate for each discrete time.</dd>
<dt>xi_0</dt>
<dd>the recovery rate of the population, must be in \((0,1)\).</dd>
<dt>user_seed</dt>
<dd>seed for random distribution generation.</dd></dl>

Arguments

Simulate epidemiological data — sim_epi_data

<dl>

<dt>S0</dt>
<dd>number of individuals in the population.</dd>


<dt>I0</dt>
<dd>number of infected individuals at time 0.</dd>


<dt>max_time</dt>
<dd>maximum observed time.</dd>


<dt>beta_vec</dt>
<dd>vector with the infection rate for each discrete time.</dd>


<dt>xi_0</dt>
<dd>the recovery rate of the population, must be in \((0,1)\).</dd>


<dt>user_seed</dt>
<dd>seed for random distribution generation.</dd>

</dl>

sim_epi_data: Simulate epidemiological data

Description

Usage

Value

Arguments

Examples