klaR v0.6-1
0
Monthly downloads
by Uwe Ligges
Classification and visualization
Miscellaneous functions for classification and
visualization developed at the Fakultaet Statistik, Technische
Universitaet Dortmund
Functions in klaR
| Name | Description | |
| errormatrix | Tabulation of prediction errors by classes | |
| friedman.data | Friedman's classification benchmark data | |
| B3 | West German Business Cycles 1955-1994 | |
| pvs | Pairwise variable selection for classification | |
| benchB3 | Benchmarking on B3 data | |
| corclust | Function to identify groups of highly correlated variables for removing correlated features from the data for further analysis. | |
| ucpm | Uschi's classification performance measures | |
| drawparti | Plotting the 2-d partitions of classification methods | |
| e.scal | Function to calculate e- or softmax scaled membership values | |
| predict.NaiveBayes | Naive Bayes Classifier | |
| NaiveBayes | Naive Bayes Classifier | |
| TopoS | Computation of criterion S of a visualization | |
| betascale | Scale membership values according to a beta scaling | |
| kmodes | K-Modes Clustering | |
| trigrid | Barycentric plots | |
| predict.locpvs | predict method for locpvs objects | |
| triplot | Barycentric plots | |
| hmm.sop | Calculation of HMM Sum of Path | |
| dkernel | Estimate density of a given kernel | |
| EDAM | Computation of an Eight Direction Arranged Map | |
| stepclass | Stepwise variable selection for classification | |
| triperplines | Barycentric plots | |
| classscatter | Classification scatterplot matrix | |
| predict.loclda | Localized Linear Discriminant Analysis (LocLDA) | |
| locpvs | Pairwise variable selection for classification in local models | |
| plineplot | Plotting marginal posterior class probabilities | |
| quadplot | Plotting of 4 dimensional membership representation simplex | |
| quadtrafo | Transforming of 4 dimensional values in a barycentric coordinate system. | |
| nm | Nearest Mean Classification | |
| GermanCredit | Statlog German Credit | |
| .dmvnorm | Density of a Multivariate Normal Distribution | |
| calc.trans | Calculation of transition probabilities | |
| countries | Socioeconomic data for the most populous countries. | |
| loclda | Localized Linear Discriminant Analysis (LocLDA) | |
| predict.pvs | predict method for pvs objects | |
| centerlines | Lines from classborders to the center | |
| predict.svmlight | Interface to SVMlight | |
| sknn | Simple k nearest Neighbours | |
| plot.NaiveBayes | Naive Bayes Plot | |
| meclight.default | Minimal Error Classification | |
| predict.meclight | Prediction of Minimal Error Classification | |
| triframe | Barycentric plots | |
| greedy.wilks | Stepwise forward variable selection for classification | |
| tripoints | Barycentric plots | |
| distmirr | Internal function to convert a distance structure to a matrix | |
| tritrafo | Barycentric plots | |
| rerange | Linear transformation of data | |
| rda | Regularized Discriminant Analysis (RDA) | |
| predict.rda | Regularized Discriminant Analysis (RDA) | |
| partimat | Plotting the 2-d partitions of classification methods | |
| svmlight | Interface to SVMlight | |
| cond.index | Calculation of Condition Indices for Linear Regression | |
| shardsplot | Plotting Eight Direction Arranged Maps or Self-Organizing Maps | |
| predict.woe | Weights of evidence | |
| woe | Weights of evidence | |
| plot.woe | Plot information values | |
| b.scal | Calculation of beta scaling parameters | |
| predict.sknn | Simple k Nearest Neighbours Classification | |
| No Results! | ||
Last month downloads
Details
| Date | 2009-11-16 |
| SystemRequirements | SVMlight |
| License | GPL-2 |
| URL | http://www.statistik.tu-dortmund.de |
| Packaged | 2009-11-16 16:02:35 UTC; ligges |
| Repository | CRAN |
| Date/Publication | 2009-11-16 19:25:17 |
| depends | base (>= 2.0.0) , MASS , R (>= 2.0.0) |
| suggests | combinat , e1071 , mlbench , rpart , scatterplot3d (>= 0.3-22) , som |
| Contributors | Nils Raabe, Karsten Luebke, Marc Zentgraf, Uwe Ligges, Gero Szepannek, Christian Roever |
Include our badge in your README
[](http://www.rdocumentation.org/packages/klaR)