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>.
c(person("Luca", "Coraggio",
role = c("cre", "aut"), email = "luca.coraggio@unina.it"),
person("Pietro", "Coretto",
role = c("aut"), email = "pcoretto@unisa.it"),
person("Angela", "Serra",
role = c("aut"), email = "angela.serra@tuni.fi"),
person("Roberto", "Tagliaferri",
role = c("ctb"), email = "robtag@unisa.it"))
Luca Coraggio [cre, aut],
Pietro Coretto [aut],
Angela Serra [aut],
Roberto Tagliaferri [ctb]
stats, graphics, Matrix, methods, parallel, foreach,
doParallel, utils

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>.

Rao-Scott Cochran-Armitage by Slices Trend Test
c(person("Joe", "Swintek", role = c("aut", "cre"), email = "swintek.joe@epa.gov"),
person("Kevin", "Flynn", role = "ctb", email = "Flynn.Kevin@epa.gov")
)
gWidgets, RGtk2, R2HTML, gWidgetsRGtk2, methods
Performs the Rao-Scott Cochran-Armitage by Slices trend test (RSCABS) used
in analysis of histopathological endpoints. It has functions for both command line
operations along with a built in GUI.
Joe Swintek [aut, cre],
Kevin Flynn [ctb]

Performs the Rao-Scott Cochran-Armitage by Slices trend test (RSCABS) used
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>.

gWidgets2, RGtk2, R2HTML, gWidgets2RGtk2, methods

Shadow-Test Approach to Computerized Adaptive Testing
c(
person("Bingnan", "Jiang", email = "bnjiangece@gmail.com", role = c("aut", "cre")),
person("ACT, Inc.", role = "cph")
)
Bingnan Jiang [aut, cre],
ACT, Inc. [cph]
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.
R (>= 3.4.0), rJava, shiny, shinycssloaders, shinyjs
Metrics, ggplot2, gridExtra, grid, methods, stats, utils
'EAPConfig.R' 'SimResult.R' 'configClasses.R' 'launchApp.R'
'runSim.R' 'scoreMethodConfig.R' 'shinyAppServer.R'
'shinyAppUI.R' 'utilFunctions.R' 'zzz.R'