Machine learning algorithms with unified interface and confusion
matrices
stats, grDevices, class, nnet, MASS, e1071, randomForest,
ipred
This package provides a unified interface to various
machine learning algorithms. Confusion matrices are provided
too.

Machine Learning Algorithms with Unified Interface and Confusion
Matrices
Philippe Grosjean [aut, cre],
Kevin Denis [aut]
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
email = "phgrosjean@sciviews.org"),
person("Kevin", "Denis", role = "aut",
email = "kevin.denis@umons.ac.be"))
A unified interface is provided to various machine learning
algorithms like LDA, QDA, k-nearest neighbour, LVQ, random forest, SVM, ... It
allows to train, test, and apply cross-validation using similar functions and
function arguments with a minimalist and clean, formula-based interface.
Missing data are threated the same way as base and stats R functions for all
algorithms, both in training and testing. Confusion matrices are also provided
with a rich set of metrics calculated and a few specific plots.

Maximum Likelihood Estimates of Gaussian Processes
Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots and sensitivity analysis.

GNU General Public License (GNU GPL) (portions of C code also fall under the MIT license)

Maximum likelihood Gaussian process modeling for
univariate and multi-dimensional outputs with diagnostic plots
and sensitivity analysis.

Maximum likelihood Gaussian process modeling for
univariate and multi-dimensional outputs with diagnostic plots.
Contact the maintainer for a package version that implements
sensitivity analysis functionality.

Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>. Contact the maintainer for a package version that includes sensitivity analysis.

Maximum likelihood Gaussian process modeling for
univariate and multi-dimensional outputs with diagnostic plots
following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>.
Contact the maintainer for a package version that includes
sensitivity analysis.