An R package for Normality in Stationary Processes

TL;DR

This paper introduces the nortsTest R package, which provides tests and visual diagnostics for assessing normality and stationarity in time series data, facilitating model diagnostics.

Contribution

The paper presents a new R package that implements multiple normality tests for stationary processes and offers visual tools for diagnostics in time series analysis.

Findings

The package includes tests by Lobato and Velasco, Epps, Psaradakis and Vavra, and random projection.

Simulated examples demonstrate test performance.

The package aids in model diagnostics for time series analysis.

Abstract

Normality is the main assumption for analyzing dependent data in several time series models, and tests of normality have been widely studied in the literature, however, the implementations of these tests are limited. The \textbf{nortsTest} package performs the tests of \textit{Lobato and Velasco, Epps, Psaradakis and Vavra} and \textit{random projection} for normality of 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. The aim of this work is to show the functionality of the package, presenting each test performance with simulated examples, and the package utility for model diagnostic in time series analysis.