soilVAE
0.1.9Supervised Variational Autoencoder Regression via 'reticulate'
Overview
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.
Install
Health
- OK2026-03-185 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 25%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
Repository
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Repository practices
6 development-tooling and community-health practices detected across 5 families in the upstream repository
Checks run against github.com/hugomachadorodrigues/soilvae on 2026-07-19.
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Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 2.0 median / 5 max
- System requirements
- 1 external
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
9 7 exported
Complexity
2.8 avg / 5 max
Call network
9 nodes / 5 edges
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Datasets
| Name | Class | Rows × Cols | Also ships in |
|---|---|---|---|
| datsoilspc | data.frame | 391 × 5 | – |
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.9Latest2026-03-18 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-03-18
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 1.3 MB / 2 files
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet