combat_harmonization

Applies the ComBat batch effect correction to vector images
in a <code>CSuperSample</code> object and reconstructs harmonized samples.

Provides statistical methods for analyzing samples of symmetric
positive definite (SPD) matrices, particularly functional connectivity
matrices from neuroimaging data. Implements Fréchet ANOVA
(Dubey and Müller (2019) <doi:10.1093/biomet/asz052>) for testing
differences between groups in metric spaces, and Riemannian ANOVA methods
that leverage tangent space geometry with classic multivariate test
statistics including Wilks' Lambda and Pillai's trace. Also includes
harmonization techniques for removing batch effects in multi-site studies:
ComBat-based harmonization (Honnorat et al. (2024)
<doi:10.1016/j.media.2023.103043>) and rigid harmonization
(Simeon et al. (2022) <doi:10.3389/fninf.2022.769274>). Builds on
'riemtan' package infrastructure for efficient computation with multiple
Riemannian metrics.

Nicolas Escobar

riemstats

Riemannian ANOVA Statistics

Jaroslaw Harezlak

combat_harmonization function

<dl><dt>super_sample</dt>
<dd>A <code>CSuperSample</code> object containing samples to harmonize.</dd></dl>

Arguments

Harmonize Vector Images Using ComBat — combat_harmonization

<dl>

<dt>super_sample</dt>
<dd>A <code>CSuperSample</code> object containing samples to harmonize.</dd>

</dl>

combat_harmonization: Harmonize Vector Images Using ComBat

Description

Usage

Value

Arguments