Packaged: 2018-12-09 20:25:31 UTC; matthewbirk

Packaged: 2019-05-28 00:54:41 UTC; matthewbirk

Title: Performance Measures for Statistical Learning
Description: Provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the 'mlr' package and were programed by several 'mlr' developers.

Package: meboot
Version: 0.0.1
Date: 2007/01/26
Title: Maximum Entropy Bootstrap for Time Series
Author: Hrishikesh D. Vinod <Vinod@fordham.edu> and Javier L�pez-de-Lacalle
Maintainer: Javier L�pez-de-Lacalle <javlacalle@yahoo.es>
Depends: R (>= 2.0.0)
Suggests: tcltk, plm, geepack
Description: This package performs maximum entropy density based
dependent data bootstrap. An algorithm is provided to create
a population of time series (ensemble) without assuming stationarity.
The reference paper (Vinod, H.D., 2004) explains how the algorithm
satisfies the ergodic theorem and the central limit theorem.
License: GPL version 2 or newer
Packaged: Fri Jan 26 18:22:39 2007; jedlobej

Package: meboot
Version: 1.0-0
Date: 2009-01-20
Title: Maximum Entropy Bootstrap for Time Series
Author: Hrishikesh D. Vinod <Vinod@fordham.edu> and Javier L\'opez-de-Lacalle
Maintainer: Javier L\'opez-de-Lacalle <javlacalle@yahoo.es>
Depends: R (>= 2.0.0), dynlm, plm, kinship, survival, splines, nlme, lattice, zoo
Suggests: tcltk, geepack, car, lmtest, boot, hdrcde
Description: This package performs maximum entropy density based
dependent data bootstrap. An algorithm is provided to create
a population of time series (ensemble) without assuming stationarity.
The reference paper (Vinod, H.D., 2004) explains how the algorithm
satisfies the ergodic theorem and the central limit theorem.
License: GPL (>=2)
Packaged: Wed Jan 21 00:33:14 2009; zeileis

Author: Hrishikesh D. Vinod <Vinod@fordham.edu> and Javier
Depends: R (>= 2.0.0), dynlm, plm, kinship, survival, splines, nlme,
dependent data bootstrap. An algorithm is provided to create a
population of time series (ensemble) without assuming
stationarity. The reference paper (Vinod, H.D., 2004) explains
how the algorithm satisfies the ergodic theorem and the central