Year: 2025
Type: article
Abstract: Normality is the central assumption for analyzing dependent data in several time series models, and the literature has widely studied normality tests. However, the implementations of these tests are limited. The nortsTest package is dedicated to fill this void. The package performs the asymptotic and bootstrap versions of the tests of Epps and Lobato and Velasco and the tests of Psaradakis and Vavra, random projections and El Bouch for normality of stationary processes. These tests are for univariate stationary processes but for El Bouch that also allows bivariate stationary processes. In addition, the package offers visual diagnostics for checking stationarity and normality assumptions for the most used time series models in several R packages. This work aims to show the package's functionality, presenting each test performance with simulated examples and the package utility for model diagnostic in time series analysis.
Source: The R Journal
DOI: 10.32614/rj-2024-008
Institutions: Aalto University, Universidad de Cantabria, University of Trento
Language: English
Topic: Data Analysis with R
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406928243