mlesnoff/Jchemo.jl

Tools for chemometrics and machine learning on high-dimensional data (e.g. Partial least squares regression/discrimination)

40
/ 100
Emerging

This project provides advanced data analysis tools, particularly for scientists and researchers working with complex chemical data. It takes in high-dimensional datasets, like spectroscopy readings, and helps you extract meaningful insights through methods like Partial Least Squares (PLS) regression and discrimination. The outcome is predictive models that can identify patterns and make forecasts, useful for anyone needing to interpret large multivariate chemical datasets.

Use this if you need to analyze large, complex chemical or spectroscopic datasets to build predictive models or classify samples.

Not ideal if your primary need is simple statistical analysis or if you are not working with high-dimensional data that benefits from chemometric approaches.

chemometrics spectroscopy materials-science data-modeling process-analytical-technology
No Package No Dependents
Maintenance 10 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 7 / 25

How are scores calculated?

Stars

25

Forks

2

Language

Julia

License

MIT

Last pushed

Mar 06, 2026

Commits (30d)

0

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