Abstract
Frequentist profile likelihoods have seen a resurgence in cosmology, offering an alternative to Bayesian methods as they can circumvent the impact of prior-volume effects. This paper presents Procoli, a fast and accessible package to obtain profile likelihoods in cosmology, available on GitHub and PyPI. Procoli seamlessly integrates with MontePython, incorporating all its available data likelihoods, as well as any modified versions of CLASS. This paper provides a comprehensive overview of the Procoli code, detailing the simulated-annealing optimizer at its core and the sequential computation of the profile. An an example, we use the early dark energy model which is afflicted by prior-volume effects to illustrate the code's features. We validate its optimizer with mock data, and compare optimization techniques for both the global minimum and the profile. Procoli further enables splitting profiles into their component contributions from individual experiments, offering nuanced insights into the data and model. As a valuable addition to the cosmologist's toolkit, Procoli supplements existing Bayesian codes, contributing to more robust parameter constraints in cosmological studies.