Package: soilVAE
Type: Package
Title: Supervised Variational Autoencoder Regression via 'reticulate'
Version: 0.1.9
Authors@R: 
    person("Hugo", "Rodrigues", email = "rodrigues.machado.hugo@gmail.com", role = c("aut", "cre"))
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: reticulate, stats
Suggests: knitr, rmarkdown, prospectr, pls
VignetteBuilder: knitr
SystemRequirements: Python (>= 3.9); TensorFlow (>= 2.13); Keras (>= 3)
URL: https://hugomachadorodrigues.github.io/soilVAE/,
        https://github.com/HugoMachadoRodrigues/soilVAE/
BugReports: https://github.com/HugoMachadoRodrigues/soilVAE/issues
NeedsCompilation: no
Packaged: 2026-03-12 21:02:56 UTC; rodrigues.h
Author: Hugo Rodrigues [aut, cre]
Maintainer: Hugo Rodrigues <rodrigues.machado.hugo@gmail.com>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2026-03-17 19:10:02 UTC
Built: R 4.4.3; ; 2026-03-27 20:03:23 UTC; windows
