FARDEEP: Fast and Robust Deconvolution of Tumor Infiltrating Lymphocyte from Expression Profiles using Least Trimmed Squares

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.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

Reverse dependencies:

Reverse suggests: SCdeconR


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