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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | MRIreduce_1.0.0.tar.gz |
770.8 KiB |
1.0.0 |
rolling linux/noble R-4.5 | MRIreduce_1.0.0.tar.gz |
771.2 KiB |
1.0.0 |
rolling source/ R- | MRIreduce_1.0.0.tar.gz |
646.7 KiB |
1.0.0 |
latest linux/jammy R-4.5 | MRIreduce_1.0.0.tar.gz |
770.8 KiB |
1.0.0 |
latest linux/noble R-4.5 | MRIreduce_1.0.0.tar.gz |
771.2 KiB |
1.0.0 |
latest source/ R- | MRIreduce_1.0.0.tar.gz |
646.7 KiB |
1.0.0 |
2026-04-26 source/ R- | MRIreduce_1.0.0.tar.gz |
646.7 KiB |
1.0.0 |
2026-04-23 source/ R- | MRIreduce_1.0.0.tar.gz |
646.7 KiB |