I would like to announce a new Julia package for chemometrics.
Jchemo.jl provides functions for data exploration and predictions in chemometrics or other domains, with focus on high dimensional data.
The package was initially designed about k-nearest neighbors locally weighted partial least squares regression and discrimination models (kNN-LWPLSR and kNN-LWPLSDA; e.g. https://doi.org/10.1002/cem.3209). It has now been expanded to many other methods for analysing high dimensional data.
Generic functions such as transform, predict, coef and summary are available.
Tuning the predictive models is facilitated by 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).
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 examples), e.g.:
?savgol
See here for more information.
The actual version is v0.1.9 and the package is regurlarly updated. Any feedback will be welcome.