Calculates item nearest neighbor imputation approach discussed by
Yeh et al. (2023)

Facilitates some of the analyses performed in studies of
behavioral economic discounting. The package supports scoring of the 27-Item Monetary Choice
Questionnaire (see Kaplan et al., 2016; <doi:10.1007/s40614-016-0070-9>), calculating k
values (Mazur's simple hyperbolic and exponential) using nonlinear regression, calculating
various Area Under the Curve (AUC) measures, plotting regression curves for both fit-to-group and
two-stage approaches, checking for unsystematic discounting
(Johnson & Bickel, 2008; <doi:10.1037/1064-1297.16.3.264>) and scoring of the
minute discounting task (see Koffarnus & Bickel, 2014; <doi:10.1037/a0035973>) using the
Qualtrics 5-trial discounting template (see the Qualtrics Minute Discounting User Guide;
<doi:10.13140/RG.2.2.26495.79527>), which is also available as a .qsf file in this package.

Brent Kaplan

beezdiscounting

Behavioral Economic Easy Discounting

Brent A. Kaplan

inn function

<dl><dt>dat</dt>
<dd>A single subject's 27-item MCQ data in long form</dd>
<dt>random</dt>
<dd>Boolean whether to insert a random draw (0 or 1) for NAs</dd>
<dt>verbose</dt>
<dd>Boolean whether to print subject and question ids pertaining
to missing data</dd></dl>

Arguments

Calculates item nearest neighbor imputation approach discussed by
Yeh et al. (2023) — inn

<dl>

<dt>dat</dt>
<dd>A single subject's 27-item MCQ data in long form</dd>


<dt>random</dt>
<dd>Boolean whether to insert a random draw (0 or 1) for NAs</dd>


<dt>verbose</dt>
<dd>Boolean whether to print subject and question ids pertaining
to missing data</dd>

</dl>

inn: Calculates item nearest neighbor imputation approach discussed by Yeh et al. (2023)

Description

Usage

Value

Arguments