Package: mda
Version: 0.4-8
Date: 2015-11-30
Author: S original by Trevor Hastie & Robert Tibshirani. Original R port by Friedrich Leisch, Kurt Hornik and Brian D. Ripley.
Maintainer: Trevor Hastie <hastie@stanford.edu>
Description: Mixture and flexible discriminant analysis, multivariate
adaptive regression splines (MARS), BRUTO, ...
Title: Mixture and Flexible Discriminant Analysis
Depends: R (>= 1.9.0), stats, class
Suggests: earth
License: GPL-2
Packaged: 2015-11-30 22:52:41 UTC; hastie
Repository: CRAN
Date/Publication: 2015-12-01 08:01:19
NeedsCompilation: yes
Package: multisom
Type: Package
Title: Clustering a Dataset using Multi-SOM Algorithm
Version: 1.0
Date: 2015-12-22
Author: Sarra Chair and Malika Charrad
Maintainer: Sarra Chair<sarra.chair@gmail.com>
Description: Implements two version of Multi-SOM algorithm namely stochastic Multi-SOM and batch Multi-SOM. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.
License: GPL-2
Depends: R (>= 3.1.3), class, kohonen
URL:
https://sites.google.com/site/malikacharrad/research/multisom-package
NeedsCompilation: no
Repository: CRAN
Packaged: 2015-12-23 20:07:40 UTC; chaira
Date/Publication: 2015-12-23 23:47:40
Package: kohonen
Version: 2.0.19
Title: Supervised and Unsupervised Self-Organising Maps
Author: Ron Wehrens
Maintainer: Ron Wehrens <ron.wehrens@gmail.com>
Description: Functions to train supervised and self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.
License: GPL (>= 2)
Depends: R (>= 2.6.0), class, MASS
NeedsCompilation: yes
Packaged: 2015-09-03 15:08:36 UTC; ron
Repository: CRAN
Date/Publication: 2015-09-04 07:30:50
Package: Modalclust
Type: Package
Title: Hierarchical Modal Clustering
Version: 0.6
Date: 2014-05-23
Author: Surajit Ray and Yansong Cheng
Maintainer: Surajit Ray <sray@math.bu.edu>
Description: Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors. These model-based non-parametric clustering techniques can extract clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.
Depends: R (>= 2.14.0), mvtnorm, zoo, class
Suggests: parallel, MASS
License: GPL-2
Packaged: 2014-05-23 14:14:17 UTC; sray
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-05-23 18:31:24
Package: EILA
Type: Package
Title: Efficient Inference of Local Ancestry
Version: 0.1-2
Date: 2013-09-09
Author: James J. Yang, Jia Li, Anne Buu, and L. Keoki Williams
Maintainer: James J. Yang <jyangstat@gmail.com>
Description: Implementation of Efficient Inference of Local Ancestry
using fused quantile regression and k-means classifier
Depends: R (>= 2.10), class, quantreg
License: GPL (>= 2)
Packaged: 2013-09-13 14:38:38 UTC; jyang
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-09-14 07:48:33
Package: ddalpha
Type: Package
Title: Depth-Based Classification and Calculation of Data Depth
Version: 1.1.3.1
Date: 2015-07-24
Authors@R: c(person("Oleksii", "Pokotylo", role=c("aut", "cre"),
email = "alexey.pokotylo@gmail.com"),
person("Pavlo", "Mozharovskyi", role=c("aut"),
email = "mozharovskyi@statistik.uni-koeln.de"),
person("Rainer", "Dyckerhoff", role=c("aut"),
email = "rainer.dyckerhoff@statistik.uni-koeln.de"))
SystemRequirements: C++11
Depends: stats, utils, graphics, grDevices, MASS, class, robustbase
Imports: Rcpp (>= 0.11.0)
LinkingTo: BH, Rcpp
Description: Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.
License: GPL-2
NeedsCompilation: yes
Packaged: 2015-07-24 15:35:40 UTC; Alexey
Author: Oleksii Pokotylo [aut, cre],
Pavlo Mozharovskyi [aut],
Rainer Dyckerhoff [aut]
Maintainer: Oleksii Pokotylo <alexey.pokotylo@gmail.com>
Repository: CRAN
Date/Publication: 2015-07-24 21:32:25
providing 
.