Packaged: 2011-06-10 16:28:57 UTC; Alessandro

Title: Organization Measures for Visual Foraging
Description: Several functions to compute indicators for organization and efficiency in visual foraging, multi-target visual search, and cancellation tasks. The current version of this package includes the following indicators: best-r, mean Inter-target Distance, Percentage Above Optimal (PAO) scan path, and intersections in the scan path. For more detailed descriptions, see Mark et al. (2004) <doi:10.1212/01.WNL.0000131947.08670.D4>.

Title: ForeCA - Forecastable Component Analysis
Author: Georg M. Goerg <gmg@stat.cmu.edu>
Maintainer: Georg M. Goerg <gmg@stat.cmu.edu>
Description: ForeCA is a new blind source separation (BSS) technique
for temporally dependent signals. Contrary to PCA or ICA it
explicitly searches for the best linear transformation that
makes the obtained sources as ''forecastable'' as possible. The
measure of forecastability Omega(x_t) is the entropy of the

Description: ForeCA is a novel dimension reduction (DR) technique for
temporally dependent signals. Contrary to other popular DR
methods, such as PCA or ICA, ForeCA explicitly searches for the
most ''forecastable'' signal. The measure of forecastability
Omega(x_t) is based on negative Shannon entropy of the spectral
density of the transformed signal. This R package provides the
main algorithms and some auxiliary function (summary, plotting,
Depends: R (>= 2.12.0), sapa, mgcv, astsa, class

Maintainer: Georg M. Goerg <im@gmge.org>
Description: Forecastable Component Analysis (ForeCA) is a novel dimension
reduction (DR) technique for temporally dependent signals. Contrary to
other popular DR methods, such as PCA or ICA, ForeCA explicitly searches
for the most ''forecastable'' signal. The measure of forecastability is
based on negative Shannon entropy of the spectral density of the
transformed signal. This R package provides the main algorithms and
auxiliary function(summary, plotting, etc) to apply ForeCA to multivariate

other popular DR methods, such as PCA or ICA, ForeCA takes time dependency
explicitly into account and searches for the most ''forecastable'' signal.
The measure of forecastability is based on the Shannon entropy of the
spectral density of the transformed signal. This R package provides the
main algorithms and auxiliary function (summary, plotting, etc.) to apply
ForeCA to multivariate time series data.
Suggests: astsa, mgcv, nlme (>= 3.1-64), testthat (>= 0.9.0), rSFA,