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B C D E F G H K L M N P Q R S T U W
| b.scal | Calculation of beta scaling parameters |
| B3 | West German Business Cycles 1955-1994 |
| benchB3 | Benchmarking on B3 data |
| betascale | Scale membership values according to a beta scaling |
| 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 for removing correlated features from the data for further analysis. |
| countries | Socioeconomic data for the most populous countries. |
| dkernel | Estimate density of a given kernel |
| drawparti | Plotting the 2-d partitions of classification methods |
| e.scal | Function to calculate e- or softmax scaled membership values |
| EDAM | Computation of an Eight Direction Arranged Map |
| errormatrix | Tabulation of prediction errors by classes |
| friedman.data | Friedman's classification benchmark data |
| GermanCredit | Statlog German Credit |
| greedy.wilks | Stepwise forward variable selection for classification |
| greedy.wilks.default | Stepwise forward variable selection for classification |
| greedy.wilks.formula | Stepwise forward variable selection for classification |
| hmm.sop | Calculation of HMM Sum of Path |
| kmodes | K-Modes Clustering |
| level_shardsplot | Plotting Eight Direction Arranged Maps or Self-Organizing Maps |
| loclda | Localized Linear Discriminant Analysis (LocLDA) |
| loclda.data.frame | Localized Linear Discriminant Analysis (LocLDA) |
| loclda.default | Localized Linear Discriminant Analysis (LocLDA) |
| loclda.formula | Localized Linear Discriminant Analysis (LocLDA) |
| loclda.matrix | Localized Linear Discriminant Analysis (LocLDA) |
| locpvs | Pairwise variable selection for classification in local models |
| meclight | Minimal Error Classification |
| meclight.data.frame | Minimal Error Classification |
| meclight.default | Minimal Error Classification |
| meclight.formula | Minimal Error Classification |
| meclight.matrix | Minimal Error Classification |
| NaiveBayes | Naive Bayes Classifier |
| NaiveBayes.default | Naive Bayes Classifier |
| NaiveBayes.formula | Naive Bayes Classifier |
| nm | Nearest Mean Classification |
| nm.data.frame | Nearest Mean Classification |
| nm.default | Nearest Mean Classification |
| nm.formula | Nearest Mean Classification |
| nm.matrix | Nearest Mean Classification |
| partimat | Plotting the 2-d partitions of classification methods |
| partimat.data.frame | Plotting the 2-d partitions of classification methods |
| partimat.default | Plotting the 2-d partitions of classification methods |
| partimat.formula | Plotting the 2-d partitions of classification methods |
| partimat.matrix | Plotting the 2-d partitions of classification methods |
| plineplot | Plotting marginal posterior class probabilities |
| plot.EDAM | Plotting Eight Direction Arranged Maps or Self-Organizing Maps |
| plot.NaiveBayes | Naive Bayes Plot |
| plot.rda | Regularized Discriminant Analysis (RDA) |
| plot.stepclass | Stepwise variable selection for classification |
| 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 |
| print.greedy.wilks | Stepwise forward variable selection for classification |
| print.kmodes | K-Modes Clustering |
| print.loclda | Localized Linear Discriminant Analysis (LocLDA) |
| print.meclight | Minimal Error Classification |
| print.pvs | Pairwise variable selection for classification |
| print.rda | Regularized Discriminant Analysis (RDA) |
| print.stepclass | Stepwise variable selection for classification |
| print.woe | Weights of evidence |
| pvs | Pairwise variable selection for classification |
| pvs.default | Pairwise variable selection for classification |
| pvs.formula | Pairwise variable selection for classification |
| quadplot | Plotting of 4 dimensional membership representation simplex |
| rda | Regularized Discriminant Analysis (RDA) |
| rda.default | Regularized Discriminant Analysis (RDA) |
| rda.formula | Regularized Discriminant Analysis (RDA) |
| shardsplot | Plotting Eight Direction Arranged Maps or Self-Organizing Maps |
| sknn | Simple k nearest Neighbours |
| sknn.data.frame | Simple k nearest Neighbours |
| sknn.default | Simple k nearest Neighbours |
| sknn.formula | Simple k nearest Neighbours |
| sknn.matrix | Simple k nearest Neighbours |
| stepclass | Stepwise variable selection for classification |
| stepclass.default | Stepwise variable selection for classification |
| stepclass.formula | Stepwise variable selection for classification |
| svmlight | Interface to SVMlight |
| svmlight.data.frame | Interface to SVMlight |
| svmlight.default | Interface to SVMlight |
| svmlight.file | Interface to SVMlight |
| svmlight.formula | Interface to SVMlight |
| svmlight.matrix | Interface to SVMlight |
| triframe | Barycentric plots |
| trigrid | Barycentric plots |
| trilines | Barycentric plots |
| triperplines | Barycentric plots |
| triplot | Barycentric plots |
| tripoints | Barycentric plots |
| tritrafo | Barycentric plots |
| ucpm | Uschi's classification performance measures |
| woe | Weights of evidence |
| woe.default | Weights of evidence |
| woe.formula | Weights of evidence |