person("Yizhen", "Xu", email = "yizhen_xu@alumni.brown.edu", role = c("aut", "cre")),
person("Tao", "Liu", email = "tliu@stat.brown.edu", role = "aut")
Description: Functions for implementing the targeted gold standard (GS) testing. You provide the true disease or treatment failure status and the risk score, tell 'TGST' the availability of GS tests and which method to use, and it returns the optimal tripartite rules. Please refer to Liu et al. (2013) <doi:10.1080/01621459.2013.810149> for more details.
Imports: ggplot2, methods, graphics, knitr

Title: Teaching GUI - Core functionality
Author: Matthias Templ, Gerlinde Dinges, Alexander Kowarik, Bernhard
Description: Teaching GUI (A Graphical User Interface to enhance and
support modern teaching methods in an interactive way.) - basic
framework. Providing a student and a trainer interface,
containing all facilities needed to create interactive
examples, checks or ask for student feedback.

X-CRAN-Comment: Orphaned on 2014-02-25 at the maintainer's request

Description: Trainings-GUI basic framework. Providing a student
interface and a trainer interface, containing all facilities
needed to create interactive examples, checks or ask for

Description: Teaching-GUI prototype, for creating TGUI content to
enhance classroom teaching supported by TGUI. A Graphical User
Interface to enhance and support modern teaching methods in an
Depends: TGUICore, tcltk, tkrplot, lattice, car

Description: Trainings-GUI prototyp, for creating TGUI content to

Authors@R: c(person("Torsten", "Hothorn", role = c("aut", "cre"),
Description: Contains data sets used on other packages I maintain.

Description: Contains data sets used in other packages Torsten Hothorn

Description: Contains data sets used in other packages I maintain.

Suggests: dplyr, gdata, plyr, trtf, tram, rms, coin, ATR, multcomp,
gridExtra, vcd, colorspace, lattice, knitr

Title: Technical Variation Elimination with Ensemble Learning
Author: Siyu Han [aut, cre], Jialing Huang [aut], Francesco Foppiano [aut], Cornelia Prehn [aut], Jerzy Adamski [aut], Karsten Suhre [aut], Giuseppe Matullo [aut], Freimut Schliess [aut], Christian Gieger [aut], Annette Peters [aut], Rui Wang-Sattler [aut]
Acknowledgments: TAI Yun-hsiu, WANG Ruoyu, CHENG Ming, GUO Yuan, LI
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. (2021) TIGER: technical variation elimination for metabolomics data using ensemble learning architecture (submitted).