Imports: digest, fs, jsonlite, purrr, rJava, sparklyr, tibble
Suggests: covr, dplyr, ggplot2, testthat

Title: Machine learning algorithms with unified interface and confusion
Imports: stats, grDevices, class, nnet, MASS, e1071, randomForest,
Description: This package provides a unified interface to various
machine learning algorithms. Confusion matrices are provided
Packaged: 2013-01-30 17:43:21 UTC; phgrosjean

Title: Machine Learning Algorithms with Unified Interface and Confusion
Description: 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.
Packaged: 2022-04-26 09:22:32 UTC; phgrosjean

Title: Maximum Likelihood Estimates of Gaussian Processes
Maintainer: Garrett M. Dancik <gdancik@iastate.edu>
Description: Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots and sensitivity analysis.
License: GNU General Public License (GNU GPL)
Packaged: Thu Sep 27 14:03:59 2007; root

Packaged: Sat Oct  6 16:37:50 2007; gdancik

License: GNU General Public License (GNU GPL) (portions of C code also fall under the MIT license)
Packaged: Fri Dec  7 14:16:28 2007; root

Packaged: Tue Dec 11 16:03:05 2007; gdancik

Packaged: Fri Feb  1 15:40:32 2008; root

Packaged: Wed Feb 13 17:55:19 2008; root

Packaged: Wed Feb 27 18:39:55 2008; root

Packaged: Thu Apr 24 09:34:09 2008; root

Maintainer: Garrett M. Dancik <dancikg@nsula.edu>
Packaged: Fri Dec 19 20:57:30 2008; root

Packaged: Sun Mar  8 10:55:15 2009; root

Packaged: Wed Dec 26 18:53:25 2007; root

Description: Maximum likelihood Gaussian process modeling for
univariate and multi-dimensional outputs with diagnostic plots