Alessandro Barbiero, Pier Alda Ferrari, Giancarlo Manzi

Imputation of Missing Values Through a Forward Imputation
Algorithm
Imputation of missing values in datasets of ordinal variables through a forward imputation algorithm

Organization Measures for Visual Foraging
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>.

ForeCA - Forecastable Component Analysis
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
spectral density of the signal.

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,
etc).
'discrete_entropy.R' 'ForeCA.EM-aux.R' 'ForeCA.EM.R'
'ForeCA-package.R' 'ForeCA-utils.R' 'mvspec2mvspectrum.R'
'mvspectrum2wcov.R' 'mvspectrum.R' 'Omega.R' 'prewhiten.R'
'quadratic_form.R' 'SDF2mvspectrum.R' 'spectral_entropy.R'
'continuous_entropy.R'

'continuous_entropy.R' 'discrete_entropy.R' 'ForeCA-package.R'
'foreca-utils.R' 'foreca.EM-aux.R' 'foreca.EM.R' 'foreca.R'
'mvspec2mvspectrum.R' 'mvspectrum.R' 'mvspectrum2wcov.R'
'Omega.R' 'quadratic_form.R' 'SDF2mvspectrum.R'
'spectral_entropy.R' 'whiten.R'