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It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature, it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis. The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) <doi:10.1016/j.neuroimage.2018.07.060>.
| Version: | 0.2 |
| Imports: | hommel, RNifti, plyr |
| Suggests: | knitr, rmarkdown |
| Published: | 2018-08-01 |
| Author: | Livio Finos, Jelle Goeman, Wouter Weeda, Jonathan Rosenblatt, Aldo Solari |
| Maintainer: | Livio Finos <livio.finos at unipd.it> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | ARIbrain results |
| Reference manual: | ARIbrain.pdf |
| Vignettes: |
Tutorial for ARIbrain package |
| Package source: | ARIbrain_0.2.tar.gz |
| Windows binaries: | r-devel: ARIbrain_0.2.zip, r-release: ARIbrain_0.2.zip, r-oldrel: ARIbrain_0.2.zip |
| macOS binaries: | r-release (arm64): ARIbrain_0.2.tgz, r-oldrel (arm64): ARIbrain_0.2.tgz, r-release (x86_64): ARIbrain_0.2.tgz, r-oldrel (x86_64): ARIbrain_0.2.tgz |
| Old sources: | ARIbrain archive |
| Reverse imports: | pARI, sumSome |
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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.