fmriAR-package: fmriAR: Fast AR and ARMA Noise Whitening for Functional MRI...

In fmriAR: Fast AR and ARMA Noise Whitening for Functional MRI (fMRI) Design and Data

fmriAR: Fast AR and ARMA Noise Whitening for Functional MRI (fMRI) Design and Data

Description

Lightweight utilities to estimate autoregressive (AR) and autoregressive moving average (ARMA) noise models from residuals and apply matched generalized least squares to whiten functional magnetic resonance imaging (fMRI) design and data matrices. The ARMA estimator follows a classic 1982 approach \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/69.1.81")}, and a restricted AR family mirrors workflows described by Cox (2012) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.neuroimage.2011.08.056")}.

Estimate AR/ARMA noise models from residuals and apply matched GLS prewhitening to fMRI design and data matrices. Run-aware and censor-aware.

Details

The fmriAR package provides efficient implementations for:

• AR and ARMA model estimation from fMRI residuals

• Run-aware and censor-aware whitening transformations

• Parcel-based parameter pooling

• Sandwich standard error computation

Author(s)

Maintainer: Bradley Buchsbaum [e-mail]

See Also

Useful links:

• https://bbuchsbaum.github.io/fmriAR/

Useful links:

• fit_noise for noise model estimation

• whiten_apply for applying whitening transformations

• whiten for one-step whitening

fmriAR documentation built on Jan. 26, 2026, 1:07 a.m.