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TIGERr  

Technical Variation Elimination with Ensemble Learning Architecture
View on CRAN: Click here


Download and install TIGERr package within the R console
Install from CRAN:
install.packages("TIGERr")

Install from Github:
library("remotes")
install_github("cran/TIGERr")

Install by package version:
library("remotes")
install_version("TIGERr", "1.0.0")



Attach the package and use:
library("TIGERr")
Maintained by
Siyu Han
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-09-02
Latest Update: 2022-01-06
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>.
How to cite:
Siyu Han (2021). TIGERr: Technical Variation Elimination with Ensemble Learning Architecture. R package version 1.0.0, https://cran.r-project.org/web/packages/TIGERr. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.1.0 (2021-09-02 09:50)
Other packages that cited TIGERr R package
View TIGERr citation profile
Other R packages that TIGERr depends, imports, suggests or enhances
Complete documentation for TIGERr
Functions, R codes and Examples using the TIGERr R package
Some associated functions: FF4_qc . compute_RSD . compute_targetVal . run_TIGER . select_variable . 
Some associated R codes: Internal.R . data.R . main.R .  Full TIGERr package functions and examples
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