Package: Dark 0.9.8

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

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Dark/json (API)

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

Peer review:

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

Datasets:
  • dark - Dark adaptation data.

On CRAN:

4.41 score 26 scripts 131 downloads 14 exports 0 dependencies

Last updated 8 years agofrom:243dff4e1a. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-winOKOct 29 2024
R-4.5-linuxOKOct 29 2024
R-4.4-winOKOct 29 2024
R-4.4-macOKOct 29 2024
R-4.3-winOKOct 29 2024
R-4.3-macOKOct 29 2024

Exports:AICcBestFitBootDarkDeclutterGetDataHModelSelectMultiStartP3P5cP6cP7cStartTestData

Dependencies:

The Parameters of a Dark Adaptation Model Explained

Rendered fromparameter_exp.Rmdusingknitr::rmarkdownon Oct 29 2024.

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

Workflow

Rendered fromWorkflow.Rmdusingknitr::rmarkdownon Oct 29 2024.

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