cderiv

Take the central derivative of numeric vectors by averaging the forward and
backward differences.

Performs a spatial Bayesian general linear model (GLM) for task
functional magnetic resonance imaging (fMRI) data on the cortical surface.
Additional models include group analysis and inference to detect thresholded
areas of activation. Includes direct support for the 'CIFTI' neuroimaging
file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F.
Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D.
Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022)
<doi:10.1016/j.neuroimage.2022.118908>.

Amanda Mejia

BayesfMRI

Spatial Bayesian Methods for Task Functional MRI Studies

Daniel Spencer

Damon Pham

David Bolin

Sarah Ryan

Yu (Ryan) Yue

cderiv function

<dl><dt>x</dt>
<dd>A numeric matrix, or a vector which will be converted to a
single-column matrix.</dd></dl>

Arguments

Central derivative — cderiv

<dl>

<dt>x</dt>
<dd>A numeric matrix, or a vector which will be converted to a
single-column matrix.</dd>

</dl>

cderiv: Central derivative

Description

Usage

Value

Arguments

Examples