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Creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types.
| Version: | 0.1.0 |
| Imports: | gsignal, pls, glmnet, Cubist, randomForest |
| Published: | 2025-10-08 |
| DOI: | 10.32614/CRAN.package.MLSP (may not be active yet) |
| Author: | Pengyuan Chen [aut, cre], Christopher Clingensmith [aut], Chenglong Ye [aut], Sabine Grunwald [aut], Katsutoshi Mizuta [aut] |
| Maintainer: | Pengyuan Chen <pch276 at uky.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | MLSP results |
| Reference manual: | MLSP.html , MLSP.pdf |
| Package source: | MLSP_0.1.0.tar.gz |
| Windows binaries: | r-devel: MLSP_0.1.0.zip, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): MLSP_0.1.0.tgz, r-oldrel (arm64): MLSP_0.1.0.tgz, r-release (x86_64): MLSP_0.1.0.tgz, r-oldrel (x86_64): MLSP_0.1.0.tgz |
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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.