MRIreduce: ROI-Based Transformation of Neuroimages into High-Dimensional
Data Frames
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 (may not be active yet) |
| 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 |
Documentation:
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