Supervised Variational Autoencoder Regression via 'reticulate' R package soilVAE version 0.1.9 Supervised latent-variable regression for high-dimensional predictors such as soil reflectance spectra. The model uses an encoder-decoder neural network with a stochastic Gaussian latent representation regularized by a Kullback-Leibler term, and a supervised prediction head trained jointly with the reconstruction objective. The implementation interfaces R with a 'Python' deep-learning backend and provides utilities for training, tuning, and prediction. Software Depends: R (>= 3.5.0) Imports: reticulate, stats Suggests: knitr, rmarkdown, prospectr, pls Hugo Rodrigues <rodrigues.machado.hugo@gmail.com> Comprehensive R Archive Network (CRAN) Hugo Rodrigues [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=soilVAE/LICENSE) 2026-03-17 application/tgz https://CRAN.R-project.org/package=soilVAE doi:10.32614/CRAN.package.soilVAE