A copula function combines uniform marginal distributions to create versatile multivariate distribution functions. The attractiveness of copula functions stems from their ability to remove the implicit dependence on multivariate normality or the assumption of independence among dimensions. We delve into the concept of copula functions, which offer a convenient means of representing joint distributions and simulating correlated variables. Numerous copulas with diverse shapes are at one’s disposal, offering flexibility in the modeling process.