Implements generalized maximum entropy estimation for linear regression, kink regression, and smooth transition kink regression models. The approach represents unknown parameters and disturbances as probability distributions over discrete support spaces and estimates them by maximizing entropy subject to model constraints. It is particularly suited to ill-posed problems and does not require distributional assumptions on the error term. The methods have been applied in empirical studies such as Tarkhamtham and Yamaka (2019) <https://thaijmath.com/index.php/thaijmath/article/view/867/870> and Maneejuk, Yamaka, and Sriboonchitta (2022) <doi:10.1080/03610918.2020.1836214>.