melt: Multiple Empirical Likelihood Tests in R

melt: Multiple Empirical Likelihood Tests in R

empirical likelihood, generalized linear models, hypothesis testing, optimization,
R

Empirical likelihood enables a nonparametric, likelihood-driven style of inference without relying on assumptions frequently made in parametric models. Empirical likelihoodbased tests are asymptotically pivotal and thus avoid explicit studentization. This paper
presents the R package melt that provides a unified framework for data analysis with
empirical likelihood methods. A collection of functions are available to perform multiple empirical likelihood tests for linear and generalized linear models in R. The package
melt offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. Hypothesis testing
uses a projected gradient algorithm to solve constrained empirical likelihood optimization
problems. The core computational routines are implemented in C++, with OpenMP for
parallel computation.

Eunseop Kim

https://www.jstatsoft.org/article/view/v108i05

The Ohio State University

February 2024

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