Authors@R: person("Markus Peter", "Auer", role = c("aut", "cre"),
Maintainer: Markus Peter Auer <mp.auer@meanerreversion.com>
Description: Beta drift poses a serious challenge to asset managers
and financial researchers. Beta drift causes problems in asset
pricing models and can have serious ramifications for hedging
attempts. Providing users with a tool that allows them to
quantify beta drift and form educated opinions about it is
This package contains the BDA() function that performs a beta
drift analysis, typically for multi-factor asset pricing models.
The BDA() function tests the underlying model parameters for
drift across time, drift across model horizon, and applies a
jackknife procedure to the baseline model. This allows the users
to draw conclusions about the stability of model parameters or
make inferences about the behavior of funds. For example, the
drift of parameters for active funds could be interpreted as
implicit style drift or, in the case of passive funds, management's
inability to track a benchmark completely.
Depends: R (>= 3.2.3), graphics, stats, xts, zoo

Title: Remove weekends and holidays from ggplot2 axes
Description: Provides a continuous date scale, omitting weekends and holidays
Packaged: 2014-10-08 20:14:28 UTC; David.Mills

Title: Remove Weekends and Holidays From ggplot2 Axes
Description: Provides a continuous date scale, omitting weekends and holidays.
Packaged: 2014-10-27 15:38:23 UTC; David.Mills

Packaged: 2014-11-13 18:02:47 UTC; David.Mills

Title: Remove Weekends and Holidays from ggplot2 Axes
person("Dave", "Mills", , "dave.a.mills@gmail.com", c("aut", "cre"))

Title: Routines for Block Diagonal Symmetric matrices
Description: This is a special case of sparse matrices, used by coxme
Collate: bdsmatrix.R gchol.R gchol.bdsmatrix.R as.matrix.bdsmatrix.R

Title: Routines for Block Diagonal Symmetric Matrices
Description: This is a special case of sparse matrices, used by coxme.

Author: Vijay Barve <vijay.barve@gmail.com>, Javier Otegui
Description: Biodiversity data visualizations using R would be helpful to
understand completeness of biodiversity inventory, extent of geographical,
taxonomic and temporal coverage, gaps and biases in data.
Imports: maps, plotrix, sqldf, plyr, taxize, treemap, ggplot2, grid