Provides mutual information estimators based on k-nearest neighbor estimators by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138>, S. Gao, et al. (2015) <http://proceedings.mlr.press/v38/gao15.pdf> and local density estimators by W. Gao, et al. (2017) <doi:10.1109/ISIT.2017.8006749>.

Simpler use of data mining methods (e.g. NN and SVM) in
classification and regression.
This package facilitates the use of data mining algorithms
in classification and regression tasks by presenting a short
and coherent set of functions. While several DM algorithms can
be used, it is particularly suited for Neural Networks (NN) and
Support Vector Machines (SVM).
nnet, kknn, kernlab, rpart, lattice, methods

nnet, kknn, kernlab, rpart, lattice, plotrix, methods

This package facilitates the use of data mining algorithms in classification and regression tasks by presenting a short and coherent set of functions. While several DM algorithms can be used, it is particularly suited for Neural Networks (NN) and Support Vector Machines (SVM). Versions: 1.3.1 minor corrections; 1.3 - new classification and regression metrics (improved mmetric function); 1.2 - new input importance methods (improved Importance function); 1.1 - minor error corrections; 1.0 - first version.
nnet, kknn, kernlab, rpart, plotrix, lattice, methods

This package facilitates the use of data mining algorithms
in classification and regression tasks by presenting a short
and coherent set of functions. While several DM algorithms can
be used, it is particularly suited for Neural Networks (NN) and
Support Vector Machines (SVM). Version 1.3 - new classification
and regression metrics (improved mmetric function); version 1.2
- new input importance methods (improved Importance function);
version 1.1 - minor error corrections; version 1.0 - first
version.
nnet, kknn, kernlab, rpart, grDevices, graphics, plotrix,
lattice, methods

Data Mining Classification and Regression Methods
Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models/algorithms, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics (improved mmetric function); 1.2 - new input importance methods (improved Importance function); 1.0 - first version.
methods, plotrix, lattice, nnet, pls, MASS, mda, rpart,
randomForest, adabag, party, Cubist, kernlab, e1071