Package: mand 3.0

Atsushi Kawaguchi

mand: Multivariate Analysis for Neuroimaging Data

Provides functions for multivariate analysis and visualization of neuroimaging data. The package contains the functions and example data used in the book 'Multivariate Analysis for Neuroimaging Data' by Kawaguchi (2021, ISBN: 978-0367255329). It includes utilities for image visualization, image data matrix construction, basis reconstruction, multicomponent visualization, predictive modeling, simulation, and multiblock analysis. Version 3.0 preserves the public interfaces used in the accompanying package vignettes.

Authors:Atsushi Kawaguchi [aut, cre]

mand_3.0.tar.gz
mand_3.0.zip(r-4.7-any)mand_3.0.zip(r-4.6-any)
mand_3.0.tar.gz(r-4.7-any)mand_3.0.tar.gz(r-4.6-any)
mand_3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mand/json (API)

# Install 'mand' in R:
install.packages('mand', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.72 score 15 scripts 492 downloads 12 exports 5 dependencies

Last updated from:6c726fd443. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK127
source / vignettesOK280
linux-releaseOK135
windows-develOK92
windows-releaseOK84
wasm-releaseOK121

Exports:atlastablebasisprodcoatimgdatamatmulticoatmulticompplotmultirecptestrbfuncrecsimbrainsizechange

Dependencies:abindbitopsoro.niftiRcppRNifti

Overview for mand
Introduction | Template | Image Data Matrix | Overlay | Atlas | Principal Component Analysis | Generate Simulation Data | Two-steps dimension reduction | Basis Expansion | Sparse PCA | Supervised Sparse PCA
Rendered froma_overview.Rmdusingknitr::rmarkdown

Last update: 2026-09-14
Started: 2021-06-29

Matrix Decomposition
Data reshape | Preparation | Generate Simulation Data | Principal Component Analysis | Two-steps dimension reduction | Methods | Sparse PCA | Supervised Sparse PCA | Characteristics | Impact seppix | Impact lambda | Penalty Functions | Parameter Selection | Other Methods | Cluster Analysis
Rendered frome_Multivariate_Approach_Matrix_Decomposition.Rmdusingknitr::rmarkdown

Last update: 2022-05-24
Started: 2021-06-29

Introduction
Preparation | Template | Image Data Matrix | Overlay | Atlas
Rendered fromb_Introduction.Rmdusingknitr::rmarkdown

Last update: 2021-06-29
Started: 2021-06-29

Brain Imaging Data
Affine transformation | Resize | Smoothing | 1D Gauss function | Preparation | Smoothing flow | FWHM and smoothed data
Rendered fromc_Brain_Imaging_Data.Rmdusingknitr::rmarkdown

Last update: 2021-06-29
Started: 2021-06-29

Common Statistical Approach
Random Field Theory | Original data and function | Differences with CDT | FWHM and cluster above CDT | Simple example for cluster size test | Cluster Level Inference | Cluster p-value | TFCE | TFCE process | FWHM and TFCE | Permutation test | Permutation based multiple correction
Rendered fromd_Common_Statistical_Approach.Rmdusingknitr::rmarkdown

Last update: 2021-06-29
Started: 2021-06-29

Prediction Model
Data reshape | Preparation | Logistic Regression Model | Support Vector Machine | Tree Model | Random Forests | Evaluation | Logistic regression model | SVM | Tree | Random Forest | Deep learning
Rendered fromf_Multivariate_Approach_Prediction_Model.Rmdusingknitr::rmarkdown

Last update: 2021-06-29
Started: 2021-06-29

Multi-block Approach
Multi-block PCA | Generate simulation data | Supervised multi-block PCA | PLS | Supervised sparse PLS
Rendered fromg_Multi-block_Approach.Rmdusingknitr::rmarkdown

Last update: 2021-06-29
Started: 2021-06-29