Package: SII
Type: Package
Title: Calculate ANSI S3.5-1997 Speech Intelligibility Index
Version: 1.0.2
Date: 2011-09-01
Author: Gregory R. Warnes
Maintainer: Gregory R. Warnes <greg@warnes.net>
Description: This package calculates ANSI S3.5-1997 Speech
Intelligibility Index (SII), a standard method for computing
the intelligibility of speech from acoustical measurements of
speech, noise, and hearing thresholds. This package includes
data frames corresponding to Tables 1 - 4 in the ANSI standard
as well as a function utilizing these tables and user-provided
hearing threshold and noise level measurements to compute the
SII score.  The methods implemented here extend the standard
computations to allow calculation of SII when the measured
frequencies do not match those required by the standard by
applying interpolation to obtain values for the required
frequencies -- Development of this package was funded by the
Center for Bioscience Education and Technology (CBET) of the
Rochester Institute of Technology (RIT).
Depends:
Suggests: splines, gdata, xtable
License: GPL-2
LazyLoad: yes
Packaged: 2011-09-01 21:58:14 UTC; warnegr1
Repository: CRAN
Date/Publication: 2011-09-02 04:53:48

This package calculates ANSI S3.5-1997 Speech Intelligibility Index
(SII), a standard method for computing the intelligibility of
speech from acoustical measurements of speech, noise, and hearing
thresholds. This package includes data frames corresponding to
Tables 1 - 4 in the ANSI standard as well as a function utilizing
these tables and user-provided hearing threshold and noise level
measurements to compute the SII score.  The methods implemented
here extend the standard computations to allow calculation of SII
when the measured frequencies do not match those required by the
standard by applying interpolation to obtain values for the
Development of this package was funded by the Center for Bioscience
Education and Technology (CBET) of the Rochester Institute of

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Description: Provides a general framework to perform statistical inference of each gene pair
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Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso
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