Package: MRIreduce
Type: Package
Title: ROI-Based Transformation of Neuroimages into High-Dimensional
        Data Frames
Version: 1.0.0
Authors@R: c(
    person("Joshua", "Milstein", email = "joshua.millstein@usc.edu", role = c("aut")),
    person("Jinyao", "Tian", email = "jinyaoti@usc.edu", role = c("aut", "cre"))
  )
Maintainer: Jinyao Tian <jinyaoti@usc.edu>
Description: 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.
License: MIT + file LICENSE
Imports: R6, Rcpp, fslr, neurobase, oro.nifti, parallel, partition,
        reshape2, reticulate
Depends: R (>= 3.5.0)
SystemRequirements: FSL (FMRIB Software Library, available at
        https://fsl.fmrib.ox.ac.uk/fsl/docs/#/install/index)
LinkingTo: Rcpp
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.3.2
Suggests: DT, EveTemplate, knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://uscbiostats.github.io/MRIreduce/
Additional_repositories: https://neuroconductor.org/releases/2020/05
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2026-04-19 04:42:57 UTC; jinyaotian
Author: Joshua Milstein [aut],
  Jinyao Tian [aut, cre]
Repository: CRAN
Date/Publication: 2026-04-21 20:42:25 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-26 19:54:57 UTC; unix
Archs: MRIreduce.so.dSYM
