| B3 | West German Business Cycles 1955-1994 |
| benchB3 | Benchmarking on B3 data |
| betascale | Scale membership values according to a beta scaling |
| b.scal | Calculation of beta scaling parameters |
| calc.trans | Calculation of transition probabilities |
| centerlines | Lines from classborders to the center |
| classscatter | Classification scatterplot matrix |
| cond.index | Calculation of Condition Indices for Linear Regression |
| corclust | Function to identify groups of highly correlated variables... |
| countries | Socioeconomic data for the most populous countries. |
| cvtree | Extracts variable cluster IDs |
| distmirr | Internal function to convert a distance structure to a matrix |
| dkernel | Estimate density of a given kernel |
| dmvnorm | Density of a Multivariate Normal Distribution |
| drawparti | Plotting the 2-d partitions of classification methods |
| EDAM | Computation of an Eight Direction Arranged Map |
| errormatrix | Tabulation of prediction errors by classes |
| e.scal | Function to calculate e- or softmax scaled membership values |
| friedmandata | Friedman's classification benchmark data |
| GermanCredit | Statlog German Credit |
| greedy.wilks | Stepwise forward variable selection for classification |
| hmm.sop | Calculation of HMM Sum of Path |
| kmodes | K-Modes Clustering |
| loclda | Localized Linear Discriminant Analysis (LocLDA) |
| locpvs | Pairwise variable selection for classification in local... |
| meclight | Minimal Error Classification |
| NaiveBayes | Naive Bayes Classifier |
| nm | Nearest Mean Classification |
| partimat | Plotting the 2-d partitions of classification methods |
| plineplot | Plotting marginal posterior class probabilities |
| plot.NaiveBayes | Naive Bayes Plot |
| plot.woe | Plot information values |
| predict.loclda | Localized Linear Discriminant Analysis (LocLDA) |
| predict.locpvs | predict method for locpvs objects |
| predict.meclight | Prediction of Minimal Error Classification |
| predict.NaiveBayes | Naive Bayes Classifier |
| predict.pvs | predict method for pvs objects |
| predict.rda | Regularized Discriminant Analysis (RDA) |
| predict.sknn | Simple k Nearest Neighbours Classification |
| predict.svmlight | Interface to SVMlight |
| predict.woe | Weights of evidence |
| pvs | Pairwise variable selection for classification |
| quadplot | Plotting of 4 dimensional membership representation simplex |
| quadtrafo | Transforming of 4 dimensional values in a barycentric... |
| rda | Regularized Discriminant Analysis (RDA) |
| rerange | Linear transformation of data |
| shardsplot | Plotting Eight Direction Arranged Maps or Self-Organizing... |
| sknn | Simple k nearest Neighbours |
| stepclass | Stepwise variable selection for classification |
| svmlight | Interface to SVMlight |
| TopoS | Computation of criterion S of a visualization |
| triframe | Barycentric plots |
| trigrid | Barycentric plots |
| triperplines | Barycentric plots |
| triplot | Barycentric plots |
| tripoints | Barycentric plots |
| tritrafo | Barycentric plots |
| ucpm | Uschi's classification performance measures |
| woe | Weights of evidence |
| xtractvars | Variable clustering based variable selection |
