Enhancing Neutrosophic Data Analysis: A Review of  Neutrosophic Measures and Applications with Neutrostat

Zahid Khan; Katrina Lane Krebs

Authors

Zahid Khan Department of Quantitative Methods, University of Pannonia, Egyetem str. 10, Veszprem, H-8200, Hungary.;
Katrina Lane Krebs SNM Social Sciences, Central Queensland University, 160 Ann St, Brisbane, QLD 4000, QLD, Australia;

Keywords:

Descriptive measures; inferential measures; neutrosophic measures; neutrosophic probability; R package

Abstract

Neutrosophic statistical measures analyze data that is not fully determined, often due to imprecise
observations. This type of data presents a major concern in neutrosophic statistics. The existing literature cat
egorizes neutrosophic measures into two types: descriptive and inferential, aiming to broaden the categorization
of statistics in these two major areas. Every statistical measure can be contextualized within the neutrosophic
framework by acknowledging the inherent imprecision, vagueness, or not fully defined. In this study, the major
focus is on reviewing existing neutrosophic measures rather than proposing new ones. The aim is to enhance
current neutrosophic structures to make them more beneficial for end users in their analysis. Additionally,
one of the contemporary challenges in neutrosophic data analysis is the dimension of data. We develop the R
library neutrostat to efficiently describe complex and larger imprecise datasets. Finally, real-world examples are
provided to review the effectiveness of the neutrostat package for analyzing neutrosophic data and evaluating
existing neutrosophic measures.

DOI: 10.5281/zenodo.14232782