onMLE

onMLE is a software package for estimating parameters of statistical models using various maximum likelihood estimation (MLE) techniques. It is designed to be a flexible and efficient tool for researchers and practitioners working with probabilistic models. The package aims to provide a unified interface for implementing common MLE algorithms, catering to a range of statistical distributions and model structures.

Key features of onMLE include support for both unconstrained and constrained optimization. It allows users to define custom likelihood functions and specify constraints on the model parameters, which is crucial for many real-world applications. The package is built with performance in mind, leveraging optimized numerical methods to handle potentially large datasets and complex models.

onMLE is particularly useful in fields such as econometrics, machine learning, and biostatistics, where model fitting and parameter estimation are core tasks. Its modular design enables users to extend its functionality or integrate it with other statistical software libraries. The development of onMLE focuses on providing clear documentation and examples to facilitate its adoption and use within the broader scientific community. Researchers can employ onMLE for tasks like fitting generalized linear models, estimating parameters of latent variable models, and performing hypothesis testing through likelihood ratio tests. The package supports a variety of optimization algorithms, including gradient-based methods and numerical approximation techniques, offering flexibility in choosing the most suitable approach for a given problem.