Neutrosophic Sets and Systems
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.
Recommended Citation
Khan, Zahid and Katrina Lane Krebs. "Enhancing Neutrosophic Data Analysis: A Review of Neutrosophic Measures and Applications with Neutrostat." Neutrosophic Sets and Systems 78, 1 (2025). https://digitalrepository.unm.edu/nss_journal/vol78/iss1/11
