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.
- 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
# install.packages("remotes") # only needed once
remotes::install_github("bbuchsbaum/fmriAR")
library(fmriAR)# 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.
- Hannan, E. J., & Rissanen, J. (1982). Recursive estimation of mixed autoregressive-moving average order. Biometrika, 69(1), 81–94.