High-Performance Open-Source Archive
Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.
| Version: | 1.0.1 |
| Depends: | R (≥ 3.3.0) |
| Imports: | nnls (≥ 1.4), stats, preprocessCore |
| Published: | 2019-04-24 |
| DOI: | 10.32614/CRAN.package.FARDEEP |
| Author: | Yuning Hao [aut], Ming Yan [aut], Blake R. Heath [aut], Yu L. Lei [aut], Yuying Xie [aut, cre] |
| Maintainer: | Yuying Xie <xyy at egr.msu.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | FARDEEP results |
| Reference manual: | FARDEEP.html , FARDEEP.pdf |
| Package source: | FARDEEP_1.0.1.tar.gz |
| Windows binaries: | r-devel: FARDEEP_1.0.1.zip, r-release: FARDEEP_1.0.1.zip, r-oldrel: FARDEEP_1.0.1.zip |
| macOS binaries: | r-release (arm64): FARDEEP_1.0.1.tgz, r-oldrel (arm64): FARDEEP_1.0.1.tgz, r-release (x86_64): FARDEEP_1.0.1.tgz, r-oldrel (x86_64): FARDEEP_1.0.1.tgz |
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