fuseMLR: Fusing Machine Learning in R

Recent technological advances have enable the simultaneous collection of multi-omics data i.e., different types or modalities of molecular data, presenting challenges for integrative prediction modeling due to the heterogeneous, high-dimensional nature and possible missing modalities of some individuals. We introduce this package for late integrative prediction modeling, enabling modality-specific variable selection and prediction modeling, followed by the aggregation of the modality-specific predictions to train a final meta-model. This package facilitates conducting late integration predictive modeling in a systematic, structured, and reproducible way.

Version:	0.0.1
Depends:	R (≥ 3.6.0)
Imports:	R6, stats, digest
Suggests:	testthat (≥ 3.0.0), UpSetR (≥ 1.4.0), caret, ranger, glmnet, Boruta, knitr, rmarkdown, pROC, checkmate
Published:	2024-12-17
DOI:	10.32614/CRAN.package.fuseMLR
Author:	Cesaire J. K. Fouodo [aut, cre]
Maintainer:	Cesaire J. K. Fouodo <cesaire.kuetefouodo at uni-luebeck.de>
BugReports:	https://github.com/imbs-hl/fuseMLR/issues
License:	GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	fuseMLR results

Documentation:

Reference manual:	fuseMLR.pdf
Vignettes:	How does fuseMLR work? (source, R code)

Downloads:

Package source:	fuseMLR_0.0.1.tar.gz
Windows binaries:	r-devel: fuseMLR_0.0.1.zip, r-release: fuseMLR_0.0.1.zip, r-oldrel: fuseMLR_0.0.1.zip
macOS binaries:	r-release (arm64): fuseMLR_0.0.1.tgz, r-oldrel (arm64): fuseMLR_0.0.1.tgz, r-release (x86_64): fuseMLR_0.0.1.tgz, r-oldrel (x86_64): fuseMLR_0.0.1.tgz

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