Title: Rmetrics - Bivariate Dependence Structures with Copulae
Depends: R (>= 2.15.1), timeDate, timeSeries, fBasics, fMultivar
Suggests: methods, RUnit, tcltk, mvtnorm, sn

Description: Provides a  collection of functions to
manage, to investigate and to analyze bivariate financial returns by
Copulae. Included are the families of Archemedean, Elliptical,

Packaged: 2017-11-16 17:28:33 UTC; Tobias Setz

Title: Dynamic Model Averaging and Dynamic Model Selection for
Imports: doParallel, forecast, foreach, gplots, graphics, grDevices,
iterators, MSwM, parallel, png, stats, utils, xts, zoo
Author: Krzysztof Drachal [aut, cre] (Faculty of Economic Sciences, University of Warsaw, Poland)
Description: It allows to estimate Dynamic Model Averaging, Dynamic Model Selection and Median Probability Model. The original methods (see References) are implemented, as well as, selected further modifications of these methods. In particular the User might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using Google Trends. The code is written in a way which minimises the computational burden (which is quite an obstacle for Dynamic Model Averaging if many variables are used). For example, this package allows for parallel computations under Windows machines. Additionally, the User might reduce a set of models according to a certain algorithm. The package is designed in a way that is hoped to be especially useful in economics and finance. (Research funded by the Polish National Science Centre grant under the contract number DEC-2015/19/N/HS4/00205.)

iterators, MSwM, parallel, png, stats, tseries, utils, xts, zoo
Description: It allows to estimate Dynamic Model Averaging, Dynamic Model Selection and Median Probability Model. The original methods (see References) are implemented, as well as, selected further modifications of these methods. In particular the User might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using Google Trends. The code is written in a way which minimises the computational burden (which is quite an obstacle for Dynamic Model Averaging if many variables are used). For example, this package allows for parallel computations under Windows machines. Additionally, the User might reduce a set of models according to a certain algorithm. The package is designed in a way that is hoped to be especially useful in economics and finance.
Note: Research funded by the Polish National Science Centre grant under