PeakPerformance employs Bayesian modeling for chromatographic peak data fitting. This has the innate advantage of providing uncertainty quantification while jointly estimating all peak parameters united in a single peak model. As Markov Chain Monte Carlo (MCMC) methods are utilized to infer the posterior probability distribution, convergence checks and the aformentioned uncertainty quantification are applied as novel quality metrics for a robust peak recognition.

Be sure to check out our thorough documentation. It contains not only information on how to install PeakPerformance and prepare raw data for its application but also detailed treatises about the implemented model structures, validation with both synthetic and experimental data against a commercially available vendor software, exemplary usage of diagnostic plots and investigation of various effects. Furthermore, you will find example notebooks and data sets showcasing different aspects of PeakPerformance.

If you encounter bugs while using PeakPerformance, please bring them to our attention by opening an issue. When doing so, describe the problem in detail and add screenshots/code snippets and whatever other helpful material you can provide. When contributing code, create a local clone of PeakPerformance, create a new branch, and open a pull request (PR).

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