estimate_nu

Universally estimate IW dof parameter nu based on method of moments, so
that no empirical variance is under-estimated

Implements Bayesian brain mapping models, including the prior
ICA (independent components analysis) model proposed in Mejia et al. (2020)
<doi:10.1080/01621459.2019.1679638> and the spatial prior ICA model
proposed in proposed in Mejia et al. (2022)
<doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level
brain as deviations from known population-level networks, which are
estimated using standard ICA algorithms. Both models employ an
expectation-maximization algorithm for estimation of the latent brain
networks and unknown model parameters. Includes direct support for 'CIFTI',
'GIFTI', and 'NIFTI' neuroimaging file formats.

Amanda Mejia

BayesBrainMap

Estimate Brain Networks and Connectivity with Population-Derived
Priors

Damon Pham

Daniel Spencer

Mary Beth Nebel

estimate_nu function

<dl><dt>var_FC</dt>
<dd>Empirical between-subject variance of covariance matrices (QxQ)</dd>
<dt>mean_FC</dt>
<dd>Empirical mean of covariance matrices (QxQ)</dd></dl>

Arguments

Universally estimate IW dof parameter nu based on method of moments, so
that no empirical variance is under-estimated — estimate_nu

<dl>

<dt>var_FC</dt>
<dd>Empirical between-subject variance of covariance matrices (QxQ)</dd>


<dt>mean_FC</dt>
<dd>Empirical mean of covariance matrices (QxQ)</dd>

</dl>

estimate_nu: Universally estimate IW dof parameter nu based on method of moments, so that no empirical variance is under-estimated

Description

Usage

Value

Arguments