Package: Dark 0.9.9

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.

Authors:Jeremiah MF Kelly [aut, cre, cph]

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NEWS

# Install 'Dark' in R:
install.packages('Dark', repos = c('https://emkayoh.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/emkayoh/dark/issues

Datasets:
  • dark - Dark adaptation data.

On CRAN:

Conda:

5.18 score 30 scripts 209 downloads 5 mentions 14 exports 0 dependencies

Last updated 2 months agofrom:445fb24060. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 25 2025
R-4.5-winOKMar 25 2025
R-4.5-macOKMar 25 2025
R-4.5-linuxOKMar 25 2025
R-4.4-winOKMar 25 2025
R-4.4-macOKMar 25 2025
R-4.4-linuxOKMar 25 2025
R-4.3-winOKMar 25 2025
R-4.3-macOKMar 25 2025

Exports:AICcBestFitBootDarkDeclutterGetDataHModelSelectMultiStartP3P5cP6cP7cStartTestData

Dependencies:

The Parameters of a Dark Adaptation Model Explained

Rendered fromparameter_exp.Rmdusingknitr::rmarkdownon Mar 25 2025.

Last update: 2016-06-02
Started: 2015-05-17

Workflow

Rendered fromWorkflow.Rmdusingknitr::rmarkdownon Mar 25 2025.

Last update: 2016-06-02
Started: 2015-05-17