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iimi

Identifying Infection with Machine Intelligence

v1.2.2 · Dec 3, 2025 · MIT + file LICENSE

Description

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.

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CRAN Check Status

14 OK
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r-devel-linux-x86_64-debian-clang OK
r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-macos-arm64 OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
r-oldrel-macos-x86_64 OK
r-oldrel-windows-x86_64 OK
r-patched-linux-x86_64 OK
r-release-linux-x86_64 OK
r-release-macos-arm64 OK
r-release-macos-x86_64 OK
r-release-windows-x86_64 OK

Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies Biostrings caret data.table dplyr GenomicAlignments IRanges mltools randomForest Rsamtools xgboost MTPS stringr R.utils Rdpack iimi

Version History

new 1.2.2 Mar 10, 2026
updated 1.2.2 ← 1.2.1 diff Dec 3, 2025
updated 1.2.1 ← 1.1.1 diff Oct 31, 2024
updated 1.1.1 ← 1.1.0 diff Jul 25, 2024
updated 1.1.0 ← 1.0.2 diff Jul 17, 2024
new 1.0.2 Mar 6, 2024