Jchemo.jl

Documentation for Jchemo.jl.

Overview

Jchemo.jl is a package for data exploration and prediction with focus on high dimensional data.

The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS) (e.g. https://doi.org/10.1002/cem.3209). Then, it has been expanded to many other methods for analyzing high dimensional data.

The name Jchemo comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains.

Functions such as transform, predict, coef and summary are available. Tuning the predictive models is facilitated by generic functions gridscore (validation dataset) and gridcv (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv and gridcvlv) and ridge regularization (gridscorelb and gridcvlb).

Examples demonstrating the package are available in project JchemoDemo, used for trainings. The datasets used in the examples come from package JchemoData.jl.

Some of the Jchemo functions (in particular those using kNN selections) use multi-threading to speed the computations. To take advantage of this, the user has to specify his relevant number of threads (e.g. from the setting menu of the VsCode Julia extension and the file settings.json).

Jchemo uses Makie for plotting. To display the plots, the user has to preliminary install and load one of the Makie's backends (e.g. CairoMakie).

Most of the functions have a help page (providing an example), e.g.:

?savgol

Before to update Jchemo, it is recommended to have a look on What changed to avoid problems due to eventual breaking changes.