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A novel machine learning method for plant viruses diagnostic using genome sequencing data. This package includes three different machine learning models, random forest, XGBoost, and elastic net, to train and predict mapped genome samples. Mappability profile and unreliable regions are introduced to the algorithm, and users can build a mappability profile from scratch with functions included in the package. Plotting mapped sample coverage information is provided.
Version: | 1.1.1 |
Depends: | R (≥ 3.5.0) |
Imports: | GenomicAlignments, IRanges, Rsamtools, data.table, mltools, randomForest, xgboost, Biostrings, stats, MTPS, R.utils, caret, stringr, dplyr, Rdpack |
Suggests: | testthat (≥ 3.0.0), knitr, rmarkdown, httr |
Published: | 2024-07-26 |
DOI: | 10.32614/CRAN.package.iimi |
Author: | Haochen Ning [aut],
Ian Boyes [aut],
Ibrahim Numanagić |
Maintainer: | Xuekui Zhang <xuekui at uvic.ca> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
CRAN checks: | iimi results |
Reference manual: | iimi.pdf |
Vignettes: |
Introduction to the iimi package |
Package source: | iimi_1.1.1.tar.gz |
Windows binaries: | r-devel: iimi_1.1.1.zip, r-release: iimi_1.1.1.zip, r-oldrel: iimi_1.1.1.zip |
macOS binaries: | r-release (arm64): iimi_1.1.1.tgz, r-oldrel (arm64): iimi_1.1.1.tgz, r-release (x86_64): iimi_1.1.1.tgz, r-oldrel (x86_64): iimi_1.1.1.tgz |
Old sources: | iimi archive |
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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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