RObust rank COrrelation COefficient and test
The rococo package provides a robust gamma rank
correlation coefficient along with a permutation-based rank
correlation test.  The rank correlation coefficient and the
test are explicitly designed for dealing with noisy numerical
data.
AllGenerics.R AllClasses.R show-methods.R rococo.R
rococo.test-methods.R

AllGenerics.R AllClasses.R show-methods.R rococo.R
rococo.test-methods.R gauss.cor.R gauss.cor.test-methods.R

The rococo package provides a robust gamma rank correlation
coefficient along with a permutation-based rank correlation test.
The rank correlation coefficient and the test are explicitly
designed for dealing with noisy numerical data.

Robust Rank Correlation Coefficient and Test
The 'rococo' package provides a robust gamma rank correlation
coefficient along with a permutation-based rank correlation test.
The rank correlation coefficient and the test are explicitly
designed for dealing with noisy numerical data.

Provides the robust gamma rank correlation coefficient as introduced by
Bodenhofer, Krone, and Klawonn (2013) <DOI:10.1016/j.ins.2012.11.026>
along with a permutation-based rank correlation test.
The rank correlation coefficient and the test are explicitly
designed for dealing with noisy numerical data.

ROC, Precision-Recall, Convex Hull and other plots.
Creates single and multiple ROC plots, precision-recall,
Bayes plots and others. The plots may be either rough or smooth
with confidence limits. Threshold values, optimum points and
other interesting points may be marked on the plots. The convex
hull for multiple curves with blending fractions for
interesting points may be obtained. The axes may have normal or
lognormal scaling.

c(person("Seung Jun", "Shin", email = "sjshin@korea.ac.kr", role = c("aut","cre")),person("Do Hyun", "Kim", email = "09dohkim@gmail.com", role = c("aut")))
We develop the entire solution paths for ROC-SVM presented by Rakotomamonjy. The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>.
Seung Jun Shin [aut, cre],
Do Hyun Kim [aut]