Author: Daniel D. Sjoberg [aut, cre, cph],

Title: Conventional Cross-validation statistics for climate-growth
Author: Zongshan Li with contributions from Jinlong Zhang
Description: This package performs several conventional
Cross-validation statistical methods for climate-growth model
in the climate reconstruction from tree rings, including Sign
Test statistic, Reduction of Error statistic, Product Mean
Test, Durbin-Watson statistic etc. This package is at its
primary stage, the functions have not been tested exhaustively
and more functions would be added in the comming days.
Packaged: 2010-12-06 06:37:08 UTC; Administrator

Author: Edward Ma, Indrajit Roy, Michael Lawrence
Description: Provides distributed data structures and simplifies
Collate: 'RcppExports.R' 'ddR.R' 'dobject.R' 'ops.R' 'pdriver.R'

X-CRAN-Comment: Orphaned on 2018-03-26 as maintainer's email address

Author: Oleksii Pokotylo, Pavlo Mozharovskyi
Description: Contains DDalpha-classifier and associated functions
Packaged: 2013-02-27 15:51:18 UTC; Mozharovskyi

Packaged: 2013-05-16 09:02:58 UTC; Mozharovskyi

Title: DDalpha-classifier, depth-calculating methods and associated
Authors@R: c(person("Oleksii", "Pokotylo", role=c("aut", "cre"),
Description: Contains methods for depth classification based on the \eqn{\alpha}-procedure and for calculation of statistical depths

Title: Depth-based Classification and Calculation of Data Depth
Description: Contains procedures for depth-based supervised learning, which are entirely nonparametric, in particular the DDalpha-procedure. 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.

Title: Depth-Based Classification and Calculation of Data Depth
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.