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The mt R package

Overview

This package provides functions for metabolomics data analysis: data preprocessing, orthogonal signal correction, PCA analysis, PCA-DA analysis, PLS-DA analysis, classification, feature selection, correlation analysis, data visualisation and re-sampling strategies.

Installation from CRAN

install.packages("mt")

Installation from github

library(devtools)
install_github("wanchanglin/mt")

Usage

See the help pages of the package for details.

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Version

Install

install.packages('mt')

Monthly Downloads

348

Version

2.0-1.21

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/wanchanglin/mt

Maintainer

Wanchang Lin

Last Published

August 19th, 2025

Functions in mt (2.0-1.21)

Feature Selection Using Kruskal-Wallis Test

Feature Selection Using SVM-RFE

Feature Selection Using Welch Test

Feature Selection by PCA

Feature Selection Using RELIEF Method

Feature Selection Using Random Forests (RF)

Multiple Comparison by 'Friedman Test' and Pairwise Comparison by 'Wilcoxon Test'

Feature Selection Using Between-Group to Within-Group (BW) Ratio

Binary Classification by Multiple Classifier

Feature Selection Using PLS

Plot Classical Multidimensional Scaling

Normality Test by Shapiro-Wilk Test

Multiple Comparison by 'ANOVA' and Pairwise Comparison by 'HSDTukey Test'

Feature Selection Using Wilcoxon Test

Feature Selection Using Signal-to-Noise Ratio (SNR)

Estimation of Multiple Classification Accuracy

List Manipulation Utilities

Get Length of Feature Subset for Validation

Missing Value Utilities

Plot Matrix-Like Object by Group

Classification with PCADA

Orthogonal Signal Correction (OSC) Approach by Wold et al.

panel.smooth.line

Panel Function for Plotting Regression Line

Orthogonal Signal Correction (OSC) Approach by Sjoblom et al.

Panel Function for Plotting Ellipse and outlier

Orthogonal Signal Correction (OSC) Approach by Wise and Gallagher.

P-values Utilities

Predict Method for Class 'plsc' or 'plslda'

Plot Method for Class 'maccest'

Plot Function for PCA with Grouped Values

Outlier detection by PCA

Plot Method for Class 'accest'

Orthogonal Signal Correction (OSC)

Plot Method for Class 'plsc' or 'plslda'

Predict Method for Class 'osc'

Save List of Data Frame or Matrix into CSV File

Predict Method for Class 'pcalda'

Classification with PLSDA

Generate Control Parameters for Resampling

Functions for Tuning Appropriate Number of Components

Generate Index of Training Samples

Plot Method for Class 'pcalda'

Statistical Summary Utilities for Two-Classes Data

Pre-process Data Set

Binary Classification

Correlation Analysis Utilities

Estimate Classification Accuracy By Resampling Method

Calculate .632 and .632+ Bootstrap Error Rate

boxplot.maccest

Boxplot Method for Class 'maccest'

boxplot.frankvali

Boxplot Method for Class 'frankvali'

Feature Ranking with Resampling Method

Multiple Feature Selection

Generate Pairwise Data Set

Assess Classification Performances

Wrapper Function for Classifiers

Rank aggregation by Borda count algorithm

Feature Selection Using ANOVA

Feature Ranking and Validation on Feature Subset

data.visualisation

Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA

Frequency and Stability of Feature Selection

Feature Selection Using Area under Receiver Operating Curve (AUC)

Estimates Feature Ranking Error Rate with Resampling

Summary Utilities