Random Samples Generation from Neutrosophic Distributions Using the neutrostat Package in R

Abstract

 Random sample generation plays a fundamental role in statistical simulation, model validation,
 and performance evaluation. In the context of neutrosophic statistics, where uncertainty, indeterminacy, and
 imprecision are explicitly modeled, generating random samples from neutrosophic probability distributions
 presents unique challenges. This work proposes a unified framework that simulates random samples from a
 variety of neutrosophic distributions using the ”neutrostat” package in R. The package offers a set of user-friendly
 functions to generate neutrosophic random samples where each observation is represented by a neutrosophic
 observation with lower and upper bounds. Finally, we provide examples for some of the well-known classes of
 neutrosophic distributions including neutrosophic exponential, neutrosophic normal, and neutrosophic Weibull
 distributions. Furthermore, we study the uses of these obtained samples for neutrosophic data analysis and
 modeling. This proposed tool provides researchers and practitioners with a powerful resource in areas where
 uncertain, vague, and imprecise data are involved. Finally, some real situations and simulation results are
 shown to demonstrate how our tool is flexible and valuable

DOI: 10.5281/zenodo.16501487