Package: TIGERr 1.0.1
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:
TIGERr_1.0.1.tar.gz
TIGERr_1.0.1.zip(r-4.5)TIGERr_1.0.1.zip(r-4.4)TIGERr_1.0.1.zip(r-4.3)
TIGERr_1.0.1.tgz(r-4.5-any)TIGERr_1.0.1.tgz(r-4.4-any)TIGERr_1.0.1.tgz(r-4.3-any)
TIGERr_1.0.1.tar.gz(r-4.5-noble)TIGERr_1.0.1.tar.gz(r-4.4-noble)
TIGERr_1.0.1.tgz(r-4.4-emscripten)TIGERr_1.0.1.tgz(r-4.3-emscripten)
TIGERr.pdf |TIGERr.html✨
TIGERr/json (API)
NEWS
| # 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
- FF4_qc - Accompanying QC samples of KORA FF4
Last updated 5 months agofrom:7feab68258. Checks:8 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 13 2025 |
| R-4.5-win | OK | Feb 13 2025 |
| R-4.5-mac | OK | Feb 13 2025 |
| R-4.5-linux | OK | Feb 13 2025 |
| R-4.4-win | OK | Feb 13 2025 |
| R-4.4-mac | OK | Feb 13 2025 |
| R-4.3-win | OK | Feb 13 2025 |
| R-4.3-mac | OK | Feb 13 2025 |
Exports:compute_RSDcompute_targetValrun_TIGERselect_variable
Dependencies:MASSpbapplyppcorrandomForest
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute RSD (relative standard deviation) | compute_RSD |
| Compute target values for ensemble learning architecture | compute_targetVal |
| Accompanying QC samples of KORA FF4 (demo data) | FF4_qc |
| Run TIGER to eliminate technical variation | run_TIGER |
| Select variables for ensemble learning architecture | select_variable |
