| abr1 | abr1 Data |
| accest | Estimate Classification Accuracy By Resampling Method |
| binest | Binary Classification |
| boot.err | Calculate .632 and .632+ Bootstrap Error Rate |
| boxplot.frankvali | Boxplot Method for Class 'frankvali' |
| boxplot.maccest | Boxplot Method for Class 'maccest' |
| classifier | Wrapper Function for Classifiers |
| cl.perf | Assess Classification Performances |
| cor.util | Correlation Analysis Utilities |
| data.visualisation | Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA |
| dat.sel | Generate Pairwise Data Set |
| df.util | Summary Utilities |
| feat.agg | Rank aggregation by Borda count algorithm |
| feat.freq | Frequency and Stability of Feature Selection |
| feat.mfs | Multiple Feature Selection |
| feat.rank.re | Feature Ranking with Resampling Method |
| frank.err | Feature Ranking and Validation on Feature Subset |
| frankvali | Estimates Feature Ranking Error Rate with Resampling |
| fs.anova | Feature Selection Using ANOVA |
| fs.auc | Feature Selection Using Area under Receiver Operating Curve... |
| fs.bw | Feature Selection Using Between-Group to Within-Group (BW)... |
| fs.kruskal | Feature Selection Using Kruskal-Wallis Test |
| fs.pca | Feature Selection by PCA |
| fs.pls | Feature Selection Using PLS |
| fs.relief | Feature Selection Using RELIEF Method |
| fs.rf | Feature Selection Using Random Forests (RF) |
| fs.rfe | Feature Selection Using SVM-RFE |
| fs.snr | Feature Selection Using Signal-to-Noise Ratio (SNR) |
| fs.welch | Feature Selection Using Welch Test |
| fs.wilcox | Feature Selection Using Wilcoxon Test |
| get.fs.len | Get Length of Feature Subset for Validation |
| grpplot | Plot Matrix-Like Object by Group |
| list.util | List Manipulation Utilities |
| maccest | Estimation of Multiple Classification Accuracy |
| mbinest | Binary Classification by Multiple Classifier |
| mc.anova | Multiple Comparison by 'ANOVA' and Pairwise Comparison by... |
| mc.fried | Multiple Comparison by 'Friedman Test' and Pairwise... |
| mc.norm | Normality Test by Shapiro-Wilk Test |
| mdsplot | Plot Classical Multidimensional Scaling |
| mv.util | Missing Value Utilities |
| osc | Orthogonal Signal Correction (OSC) |
| osc_sjoblom | Orthogonal Signal Correction (OSC) Approach by Sjoblom et al. |
| osc_wise | Orthogonal Signal Correction (OSC) Approach by Wise and... |
| osc_wold | Orthogonal Signal Correction (OSC) Approach by Wold et al. |
| panel.elli | Panel Function for Plotting Ellipse and outlier |
| panel.smooth.line | Panel Function for Plotting Regression Line |
| pcalda | Classification with PCADA |
| pca.outlier | Outlier detection by PCA |
| pcaplot | Plot Function for PCA with Grouped Values |
| plot.accest | Plot Method for Class 'accest' |
| plot.maccest | Plot Method for Class 'maccest' |
| plot.pcalda | Plot Method for Class 'pcalda' |
| plot.plsc | Plot Method for Class 'plsc' or 'plslda' |
| plsc | Classification with PLSDA |
| predict.osc | Predict Method for Class 'osc' |
| predict.pcalda | Predict Method for Class 'pcalda' |
| predict.plsc | Predict Method for Class 'plsc' or 'plslda' |
| preproc | Pre-process Data Set |
| pval.util | P-values Utilities |
| save.tab | Save List of Data Frame or Matrix into CSV File |
| stats.util | Statistical Summary Utilities for Two-Classes Data |
| trainind | Generate Index of Training Samples |
| tune.func | Functions for Tuning Appropriate Number of Components |
| valipars | Generate Control Parameters for Resampling |
