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 package was named Jchemo since it 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 of these functions are also available for models based on latent variables (LVs) (gridscorelv and gridcvlv) and ridge regularization (gridscorelb and gridcvlb).

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

?savgol

Examples demonstrating Jchemo.jl are available in project JchemoDemo, used for training only. The datasets of the examples are stored in package JchemoData.jl.

Some of the functions of the package (in particular those using kNN selections) use multi-threading to speed the computations. Taking advantage of this requires to specify a relevant number of threads (e.g. from the 'Settings' menu of the VsCode Julia extension and the file 'settings.json').

Jchemo.jl uses Makie.jl for plotting. To install and load one of the Makie's backends (e.g. CairoMakie.jl) is required to display the plots.

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

Modules = [Jchemo]
Order   = [:function, :type]