| hddplot-package | Use Known Groups in High-Dimensional Data to Derive Scores for Plots |
| accTrainTest | Two subsets of data each take in turn the role of test set |
| aovFbyrow | calculate aov F-statistic for each row of a matrix |
| cvdisc | Cross-validated accuracy, in linear discriminant calculations |
| cvscores | For high-dimensional data with known groups, derive scores for plotting |
| defectiveCVdisc | defective accuracy assessments from linear discriminant calculations |
| divideUp | Partition data into mutiple nearly equal subsets |
| Golub | Golub data (7129 rows by 72 columns), after normalization |
| golubInfo | Classifying factors for the 72 columns of the Golub data set |
| hddplot | Use Known Groups in High-Dimensional Data to Derive Scores for Plots |
| orderFeatures | Order features, based on their ability to discriminate |
| pcp | convenience version of the singular value decomposition |
| plotTrainTest | Plot predictions for both a I/II train/test split, and the reverse |
| qqthin | a version of qqplot() that thins out points that overplot |
| scoreplot | Plot discriminant function scores, with various identification |
| simulateScores | Generate linear discriminant scores from random data, after selection |