Title: Dandelion Plot for R-Mode Exploratory Factor Analysis
Description: Contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis.

Title: Functions for Wayne W. Daniel's Biostatistics, Tenth Edition
Description: Functions to accompany Wayne W. Daniel's Biostatistics: A Foundation for Analysis in the Health Sciences,
Authors@R: c(person(given = 'Tingting', family = 'Zhan', role = c('aut', 'cre', 'cph'), email

Description: Functions to accompany Wayne W. Daniel's Biostatistics: A Foundation for Analysis in the Health Sciences, Tenth Edition.

Imports: e1071, latex2exp, pracma, scales

Imports: e1071, ggrepel, latex2exp, methods, pracma, scales

Author: Tingting Zhan [aut, cre, cph] (<https://orcid.org/0000-0001-9971-4844>)
Description: Functions to accompany Wayne W. Daniel's
Biostatistics: A Foundation for Analysis in the Health
Authors@R: c(person(given = 'Tingting', family = 'Zhan', role
'tingtingzhan@gmail.com', comment = c(ORCID =
Packaged: 2022-09-15 15:13:16 UTC; tingtingzhan

Title: The Analysis of Dark Adaptation Data
Maintainer: Dr Jeremiah MF Kelly <emkayoh@mac.com>
Description: 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

Maintainer: Jeremiah MF Kelly <emkayoh@mac.com>
Description: The recovery of visual sensitivity in a dark environment is known
as dark adaptation. In a clinical or research setting the recovery is typically
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.