The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

fmriAR

fmriAR provides fast AR/ARMA-based prewhitening for fMRI GLM workflows. It estimates voxel-wise or parcel-based noise models, applies segment-aware whitening, and exposes diagnostics that make it easy to confirm residual independence.

Key capabilities

• Automatic AR/ARMA order selection via Hannan–Rissanen initialization and iterative refinement (Hannan & Rissanen, 1982)
• Segment-aware whitening that respects run boundaries and optional multiscale pooling across parcels
• Convenience helpers to whiten design matrices and inspect autocorrelation diagnostics

Installation

# install.packages("remotes")  # only needed once
remotes::install_github("bbuchsbaum/fmriAR")
library(fmriAR)

Quick start

# X: design matrix (n x p), Y: voxel data (n x v), runs: factor or integer run labels
res   <- Y - X %*% qr.solve(X, Y)                      # pre-fit residuals
plan  <- fit_noise(res, runs = runs, method = "ar",    # estimate AR model
                   p = "auto", pooling = "global")
xyw   <- whiten_apply(plan, X, Y, runs = runs)         # whiten design and data
fit   <- lm.fit(xyw$X, xyw$Y)
se    <- sandwich_from_whitened_resid(xyw$X, xyw$Y, beta = fit$coefficients)
ac    <- acorr_diagnostics(xyw$Y - xyw$X %*% fit$coefficients)

See vignettes/ and ?fit_noise for more detailed workflows, including multiscale pooling and ARMA whitening.

References

• Hannan, E. J., & Rissanen, J. (1982). Recursive estimation of mixed autoregressive-moving average order. Biometrika, 69(1), 81–94.

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.
Health stats visible at Monitor.