R: Implementation of the SVM-Maj Algorithm

# Implementation of the SVM-Maj Algorithm

## Documentation for package ‘SVMMaj’ version 0.2.9.4

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

auc | Returns the area under the curve value
AusCredit | Australian Credit Approval Dataset
AusCredit.te | Australian Credit Approval Dataset
AusCredit.tr | Australian Credit Approval Dataset
classification | Show the classification performance
diabetes | Pima Indians Diabetes Data Set
diabetes.te | Pima Indians Diabetes Data Set
diabetes.tr | Pima Indians Diabetes Data Set
getHinge | Hinge error function of SVM-Maj
isb | I-spline basis of each column of a given matrix
isplinebasis | Transform a given data into I-splines
normalize | Normalize/standardize the columns of a matrix
plot.hinge | Plot the hinge function
plot.svmmaj | Print Svmmaj class
plot.svmmajcrossval | Plot the cross validation output
plotWeights | Plot the weights of all attributes from the trained SVM model
predict.svmmaj | Out-of-Sample Prediction from Unseen Data.
predict.transDat | Perform the transformation based on predefined settings
print.hinge | Hinge error function of SVM-Maj
print.q.svmmaj | SVM-Maj Algorithm
print.summary.svmmaj | Print Svmmaj class
print.svmmaj | Print Svmmaj class
print.svmmajcrossval | Print SVMMaj cross validation results
roccurve | Plot the ROC curve of the predicted values
summary.svmmaj | Print Svmmaj class
summary.svmmajcrossval | Print SVMMaj cross validation results
supermarket1996 | Supermarket data 1996
svmmaj | SVM-Maj Algorithm
svmmaj.default | SVM-Maj Algorithm
svmmajcrossval | k-fold Cross-Validation of SVM-Maj
transformdata | Transform the data with normalization and/or spline basis
voting | Congressional Voting Records Data Set
voting.te | Congressional Voting Records Data Set
voting.tr | Congressional Voting Records Data Set
X.svmmaj | Returns transformed attributes