methods available in test bench. One/two step ahead prediction is also possible

Title: Prediction for Future Data from any Continuous Distribution
Authors@R: c(person("H. ", "M. Barakat", role = c("aut"), email = "hbarakat2@hotmail.com"), person("O. ", "M. Khaled", role = c("aut"), email = "osam87@yahoo.com"), person("Hadeer ", "A. Ghonem", role = c("aut", "cre"), email = "hadeer_ghonem15@yahoo.com"))
Maintainer: Hadeer A. Ghonem <hadeer_ghonem15@yahoo.com>
Description: Functions to get prediction intervals and prediction points of future observations from any continuous distribution.

Title: Prediction Intervals for Three Basic Statistical Models
Author: Vladimir Vovk <vovk@cs.rhul.ac.uk> and Ilia Nouretdinov <ilia@cs.rhul.ac.uk>
Description: Three prediction algorithms described in the paper "On-line predictive linear regression"
Packaged: Wed Jun 25 21:27:34 2008; vovk

Packaged: Wed Jul  2 16:18:30 2008; vovk

Description: Three prediction algorithms described in the paper
Annals of Statistics 37, 1566 - 1590 (2009)

Author: Vladimir Vovk <vovk@cs.rhul.ac.uk> and Ilia Nouretdinov
"On-line predictive linear regression" Annals of Statistics 37,

Description: This package provides a set of functions useful when evaluating the results of presence-absence models. Package includes functions for calculating threshold dependant measures such as confusion matrices, pcc, sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It will calculate optimal threshold choice according to a choice of optimization criteria. It also includes functions to plot the threshold independent ROC curves along with the associated AUC (area under the curve).
License: This code was written and prepared by a U.S. Government employee on official time, and therefore it is in the public domain and not subject to copyright.
Packaged: Thu Feb 22 14:37:53 2007; efreeman

Packaged: Tue Sep 11 15:41:22 2007; eafreeman

Packaged: Mon Jan  7 12:12:06 2008; eafreeman

Description: Provides a set of functions useful when evaluating the results of presence-absence models. Package includes functions for calculating threshold dependent measures such as confusion matrices, pcc, sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It will calculate optimal threshold choice according to a choice of optimization criteria. It also includes functions to plot the threshold independent ROC curves along with the associated AUC (area under the curve).
Packaged: 2022-02-28 23:33:07 UTC; eafreeman