Description: Performs robust and sparse correlation matrix estimation. Robustness is achieved based on a simple robust pairwise correlation estimator, while sparsity is obtained based on thresholding. The optimal thresholding is tuned via cross-validation. See Serra, Coretto, Fratello and Tagliaferri (2018) <doi:10.1093/bioinformatics/btx642>.

Title: Rao-Scott Cochran-Armitage by Slices Trend Test
Authors@R: c(person("Joe", "Swintek", role = c("aut", "cre"), email = "swintek.joe@epa.gov"),
person("Kevin", "Flynn", role = "ctb", email = "Flynn.Kevin@epa.gov")
Imports: gWidgets, RGtk2, R2HTML, gWidgetsRGtk2, methods
Description: Performs the Rao-Scott Cochran-Armitage by Slices trend test (RSCABS) used
in analysis of histopathological endpoints. It has functions for both command line

in analysis of histopathological endpoints, built to be used with either a GUI or by a
command line. The RSCABS method is detailed in "Statistical analysis of
histopathological endpoints" by John Green et. al. (2014) <doi:10.1002/etc.2530>.

Imports: gWidgets2, RGtk2, R2HTML, gWidgets2RGtk2, methods

Title: Shadow-Test Approach to Computerized Adaptive Testing
person("Bingnan", "Jiang", email = "bnjiangece@gmail.com", role = c("aut", "cre")),
Description: As an advanced approach to computerized adaptive testing (CAT),
shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically
assembles entire shadow tests as a part of
selecting items throughout the testing process.
Selecting items from shadow tests guarantees the compliance of all content
constraints defined by the blueprint. 'RSCAT' is an R package for the
shadow-test approach to CAT. The objective of
'RSCAT' is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation;
2) Contributing to the academic and scientific community for CAT research.
Depends: R (>= 3.4.0), rJava, shiny, shinycssloaders, shinyjs
Imports: Metrics, ggplot2, gridExtra, grid, methods, stats, utils
Collate: 'EAPConfig.R' 'SimResult.R' 'configClasses.R' 'launchApp.R'