waic

waic.remstimate

A function that returns the WAIC (Watanabe-Akaike's Information Criterion) value in a 'remstimate' object.

A comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.

Giuseppe Arena

remstimate

Optimization Frameworks for Tie-Oriented and Actor-Oriented
Relational Event Models

Rumana Lakdawala

Fabio Generoso Vieira

Marlyne Meijerink-Bosman

Diana Karimova

Mahdi Shafiee Kamalabad

Roger Leenders

Joris Mulder

waic function

<dl><dt>object</dt>
<dd>is a <code>remstimate</code> object.</dd>
<dt>...</dt>
<dd>further arguments to be passed to the 'waic' method.</dd></dl>

Arguments

<ul>
<li><code>waic(remstimate)</code>: WAIC (Watanabe-Akaike's Information Criterion) value of a 'remstimate' object</li>
</ul>

Methods (by class)

waic — waic

<dl>

<dt>object</dt>
<dd>is a <code>remstimate</code> object.</dd>


<dt>...</dt>
<dd>further arguments to be passed to the 'waic' method.</dd>

</dl>

waic: waic

Description

Usage

Value

Arguments

Methods (by class)

Examples