Package: TIGERr
Type: Package
Title: Technical Variation Elimination with Ensemble Learning
        Architecture
Version: 1.0.2
Author: Siyu Han [aut, cre], Jialing Huang [aut], Francesco Foppiano
        [aut], Cornelia Prehn [aut], Jerzy Adamski [aut], Karsten Suhre
        [aut], Ying Li [aut], Giuseppe Matullo [aut], Freimut Schliess
        [aut], Christian Gieger [aut], Annette Peters [aut], Rui
        Wang-Sattler [aut]
Maintainer: Siyu Han <han.siyu@outlook.com>
Acknowledgments: TAI Yun-hsiu, WANG Ruoyu, CHENG Ming, GUO Yuan, LI
        Han, FAN Linrui
Description: The R implementation of TIGER. TIGER integrates random
        forest algorithm into an innovative ensemble learning
        architecture. Benefiting from this advanced architecture, TIGER
        is resilient to outliers, free from model tuning and less
        likely to be affected by specific hyperparameters. TIGER
        supports targeted and untargeted metabolomics data and is
        competent to perform both intra- and inter-batch technical
        variation removal. TIGER can also be used for cross-kit
        adjustment to ensure data obtained from different analytical
        assays can be effectively combined and compared. Reference: Han
        S. et al. (2022) <doi:10.1093/bib/bbab535>.
License: GPL (>= 3)
Depends: R (>= 3.5.0)
Imports: parallel (>= 2.1.0), pbapply (>= 1.4-3), ppcor (>= 1.1),
        randomForest (>= 4.6-14), stats (>= 3.0.0)
BugReports: https://github.com/HAN-Siyu/TIGER/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.1
Repository: https://han-siyu.r-universe.dev
Date/Publication: 2025-08-06 22:18:42 UTC
RemoteUrl: https://github.com/han-siyu/tiger
RemoteRef: HEAD
RemoteSha: 8227544bfce0f1d89e0317ce20cdbd31128d5726
NeedsCompilation: no
Packaged: 2026-07-02 06:20:17 UTC; root