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'
cl.rate                 Assess Classification Performances
classifier              Wrapper Function for Classifiers
cor.cut                 Correlation Analysis Utilities
dat.sel                 Generate Pairwise Data Set
df.summ                 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 (AUC)
fs.bw                   Feature Selection Using Between-Group to
                        Within-Group (BW) Ratio
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
list2df                 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 'HSDTukey Test'
mc.fried                Multiple Comparison by 'Friedman Test' and
                        Pairwise Comparison by 'Wilcoxon Test'
mc.norm                 Normality Test by Shapiro-Wilk Test
mdsplot                 Plot Classical Multidimensional Scaling
mv.stats                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 Gallagher.
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
pca.outlier             Outlier detection by PCA
pca_plot_wrap           Grouped Data Visualisation by PCA, MDS, PCADA
                        and PLSDA
pcalda                  Classification with PCADA
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.test               P-values Utilities
save.tab                Save List of Data Frame or Matrix into CSV File
stats.mat               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
