Dark: The Analysis of Dark Adaptation Data

The recovery of visual sensitivity in a dark environment is known as dark adaptation. In a clinical or research setting the recovery is typically measured after a dazzling flash of light and can be described by the Mahroo, Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take dark adaptation data and use nonlinear regression to find the parameters of the model that 'best' describe the data. They do this by firstly, generating rapid initial objective estimates of data adaptation parameters, then a multi-start algorithm is used to reduce the possibility of a local minimum. There is also a bootstrap method to calculate parameter confidence intervals. The functions rely upon a 'dark' list or object. This object is created as the first step in the workflow and parts of the object are updated as it is processed.

Version: 0.9.8
Imports: stats, grDevices, graphics, utils
Suggests: knitr, rmarkdown, testthat
Published: 2016-06-02
Author: Jeremiah MF Kelly
Maintainer: Jeremiah MF Kelly <emkayoh at mac.com>
BugReports: https://github.com/emkayoh/Dark/issues
License: GPL-3
URL: https://github.com/emkayoh/Dark, http://www.nihr.ac.uk
NeedsCompilation: no
Materials: README
CRAN checks: Dark results


Reference manual: Dark.pdf
Vignettes: Workflow
Parameters Explained
Package source: Dark_0.9.8.tar.gz
Windows binaries: r-devel: Dark_0.9.8.zip, r-release: Dark_0.9.8.zip, r-oldrel: Dark_0.9.8.zip
OS X El Capitan binaries: r-release: Dark_0.9.8.tgz
OS X Mavericks binaries: r-oldrel: Dark_0.9.8.tgz
Old sources: Dark archive


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