Package: TIGERr 1.0.2

TIGERr: Technical Variation Elimination with Ensemble Learning Architecture

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>.

Authors: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]

TIGERr_1.0.2.tar.gz
TIGERr_1.0.2.zip(r-4.7-any)TIGERr_1.0.2.zip(r-4.6-any)TIGERr_1.0.2.zip(r-4.5-any)
TIGERr_1.0.2.tgz(r-4.6-any)TIGERr_1.0.2.tgz(r-4.5-any)
TIGERr_1.0.2.tar.gz(r-4.7-any)TIGERr_1.0.2.tar.gz(r-4.6-any)
TIGERr_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
TIGERr/json (API)

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

Bug tracker:https://github.com/han-siyu/tiger/issues

Datasets:
  • FF4_qc - Accompanying QC samples of KORA FF4

On CRAN:

Conda:

3.54 score 7 stars 302 downloads 4 exports 4 dependencies

Last updated from:8227544bfc. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK119
source / vignettesOK184
linux-releaseOK113
macos-releaseOK125
macos-oldrelOK153
windows-develOK63
windows-releaseOK58
windows-oldrelOK57
wasm-releaseOK97

Exports:compute_RSDcompute_targetValrun_TIGERselect_variable

Dependencies:MASSpbapplyppcorrandomForest