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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.

Version: 0.1.9
Depends: R (≥ 3.5.0)
Imports: reticulate, stats
Suggests: knitr, rmarkdown, prospectr, pls
Published: 2026-03-17
DOI: 10.32614/CRAN.package.soilVAE
Author: Hugo Rodrigues [aut, cre]
Maintainer: Hugo Rodrigues <rodrigues.machado.hugo at gmail.com>
BugReports: https://github.com/HugoMachadoRodrigues/soilVAE/issues
License: MIT + file LICENSE
URL: https://hugomachadorodrigues.github.io/soilVAE/, https://github.com/HugoMachadoRodrigues/soilVAE/
NeedsCompilation: no
SystemRequirements: Python (>= 3.9); TensorFlow (>= 2.13); Keras (>= 3)
Citation: soilVAE citation info
Materials: README, NEWS
CRAN checks: soilVAE results

Documentation:

Reference manual: soilVAE.html , soilVAE.pdf
Vignettes: soilVAE vignettes (source)
soilVAE Workflow (source, R code)

Downloads:

Package source: soilVAE_0.1.9.tar.gz
Windows binaries: r-devel: soilVAE_0.1.9.zip, r-release: soilVAE_0.1.9.zip, r-oldrel: soilVAE_0.1.9.zip
macOS binaries: r-release (arm64): soilVAE_0.1.9.tgz, r-oldrel (arm64): soilVAE_0.1.9.tgz, r-release (x86_64): soilVAE_0.1.9.tgz, r-oldrel (x86_64): soilVAE_0.1.9.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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