LL_SQUAREM

Helper function for SQUAREM for extracting log likelihood

internal

Implements the template ICA (independent components analysis) model
proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the
spatial template 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

templateICAr

Estimate Brain Networks and Connectivity with ICA and Empirical
Priors

Damon Pham

Daniel Spencer

Mary Beth Nebel

LL_SQUAREM function

<dl><dt>theta_vec</dt>
<dd>Vector of current parameter values</dd></dl>

Arguments

Log-likelihood SQUAREM — LL_SQUAREM

<dl>

<dt>theta_vec</dt>
<dd>Vector of current parameter values</dd>

</dl>

LL_SQUAREM: Log-likelihood SQUAREM

Description

Usage

Value

Arguments