Package: soilVAE 0.1.9

soilVAE: Supervised Variational Autoencoder Regression via 'reticulate'

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.

Authors:Hugo Rodrigues [aut, cre]