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Converts NIfTI format T1/FL neuroimages into structured, high-dimensional 2D data frames with a focus on region of interest (ROI) based processing. The package incorporates the partition algorithm, which offers a flexible framework for agglomerative partitioning based on the Direct-Measure-Reduce approach. This method ensures that each reduced variable maintains a user-specified minimum level of information while remaining interpretable, as each maps uniquely to one variable in the reduced dataset. The partition framework is described in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>. The package allows customization in variable selection, measurement of information loss, and data reduction methods for neuroimaging analysis and machine learning workflows.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | R6, Rcpp, fslr, neurobase, oro.nifti, parallel, partition, reshape2, reticulate |
| LinkingTo: | Rcpp |
| Suggests: | DT, EveTemplate, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2026-04-21 |
| DOI: | 10.32614/CRAN.package.MRIreduce |
| Author: | Joshua Milstein [aut], Jinyao Tian [aut, cre] |
| Maintainer: | Jinyao Tian <jinyaoti at usc.edu> |
| License: | MIT + file LICENSE |
| URL: | https://uscbiostats.github.io/MRIreduce/ |
| NeedsCompilation: | yes |
| SystemRequirements: | FSL (FMRIB Software Library, available at https://fsl.fmrib.ox.ac.uk/fsl/docs/#/install/index) |
| Additional_repositories: | https://neuroconductor.org/releases/2020/05 |
| Language: | en-US |
| Materials: | README |
| CRAN checks: | MRIreduce results |
| Reference manual: | MRIreduce.html , MRIreduce.pdf |
| Vignettes: |
Introduction to MRIreduce (source, R code) |
| Package source: | MRIreduce_1.0.0.tar.gz |
| Windows binaries: | r-release: MRIreduce_1.0.0.zip, r-oldrel: MRIreduce_1.0.0.zip |
| macOS binaries: | r-release (arm64): MRIreduce_1.0.0.tgz, r-oldrel (arm64): MRIreduce_1.0.0.tgz, r-release (x86_64): MRIreduce_1.0.0.tgz, r-oldrel (x86_64): MRIreduce_1.0.0.tgz |
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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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