normalization

Centers and scales each row of a matrix to have zero mean and unit norm.

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

normalization function

<dl><dt>si</dt>
<dd>A numeric matrix.</dd></dl>

Arguments

Normalize Rows of a Matrix — normalization

<dl>

<dt>si</dt>
<dd>A numeric matrix.</dd>

</dl>

normalization: Normalize Rows of a Matrix

Description

Usage

Value

Arguments