tempodisco

tempodisco: an R package for temporal discounting - Published in JOSS (2025)

https://github.com/kinleyid/tempodisco

Scientific Fields

Repository

An R package for temporal discounting

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# tempodisco



[![DOI](https://joss.theoj.org/papers/10.21105/joss.07472/status.svg)](https://doi.org/10.21105/joss.07472) [![R-CMD-check](https://github.com/kinleyid/tempodisco/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kinleyid/tempodisco/actions/workflows/R-CMD-check.yaml) [![codecov](https://codecov.io/github/kinleyid/tempodisco/graph/badge.svg?token=CCQXS3SNGB)](https://app.codecov.io/github/kinleyid/tempodisco)



[`tempodisco`](https://kinleyid.github.io/tempodisco/index.html) is an R package for behavioural researchers working with delay discounting data (also known as temporal discounting intertemporal choice data). It implements common tasks such as scoring responses (e.g. computing indifference points from an adjusting amounts procedure, computing the "area under the curve", or computing $k$ values as in the Monetary Choice Questionnaire; [Frye et al., 2016](https://doi.org/10.3791/53584); [Myerson et al., 2001](https://doi.org/10.1901/jeab.2001.76-235); [Kirby et al., 1999](https://doi.org/10.1037//0096-3445.128.1.78)), identifying poor-quality data (e.g. failed attention checks and non-systematic responding; [Johnson & Bickel, 2008](https://doi.org/10.1037/1064-1297.16.3.264)), modelling choice data using multiple discount functions (e.g. hyperbolic, exponential, etc.---see below; [Franck et al., 2015](https://doi.org/10.1002/jeab.128)), and modelling reaction times using drift diffusion models ([Peters & D'Esposito, 2020](https://doi.org/10.1371/journal.pcbi.1007615)).

## Installation

You can install `tempodisco` from GitHub with:

``` r
# install.packages("devtools")
devtools::install_github("kinleyid/tempodisco")
```

## Getting started

See the [documentation](https://kinleyid.github.io/tempodisco/), particularly the "[Getting started](https://kinleyid.github.io/tempodisco/articles/tempodisco.html)" page, for example usage.

## Overview

A good practice in delay discounting research is to not assume that the same discount function describes every individual ([Franck et al., 2015](https://doi.org/10.1002/jeab.128)). `tempodisco` implements the following discount functions and can automatically select the best one for a given individual according to the Bayesian information criterion ([Schwartz, 1978](https://doi.org/10.1214/aos/1176344136)):

```{r child="man/fragments/predefined-discount-functions.Rmd"}
```

These discount functions can be fit to indifference point data (see [`td_ipm`](https://kinleyid.github.io/tempodisco/reference/td_ipm.html)), choice-level data (see [`td_bcnm`](https://kinleyid.github.io/tempodisco/reference/td_bcnm.html)), or data including both choices and reaction times (see [`td_ddm`](https://kinleyid.github.io/tempodisco/reference/td_ddm.html)).

After fitting a model, we can check to see how well it matches the data using the [`plot()`](https://kinleyid.github.io/tempodisco/reference/plot.td_um.html) function:

```{r}
library(tempodisco)
data("td_bc_single_ptpt")
mod <- td_bcnm(td_bc_single_ptpt, discount_function = c('hyperbolic', 'exponential'))
plot(mod, p_lines = c(0.1, 0.9), log = 'x', verbose = F)
```

Note that the discount curve contains an inflection point because the x-axis is on a log scale. See the "[Visualizing models](https://kinleyid.github.io/tempodisco/articles/visualizing-models.html)" page of the documentation for more examples.

## Further reading

The "Examples" tab on [the documentation](https://kinleyid.github.io/tempodisco/) contains a list of tutorials on solving common problems in delay discounting research.

## Reporting issues and requesting features

If you encounter problems with the software or would like to it to have additional functionality, please open a new issue on the GitHub repository. Try to include as much detail as possible, especially how to reproduce any errors/incorrect results. GitHub has instructions on opening an issue [here](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/creating-an-issue).

## Contributing

If you would like to contribute to `tempodisco`, you're more than welcome! Please follow the instructions [here](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) on how to contribute to a project on GitHub. Feel free to [contact me](https://kinleyid.github.io) if you'd like help with any contributions.

Owner

JOSS Publication

tempodisco: an R package for temporal discounting

Published

April 08, 2025

DOI

10.21105/joss.07472

Volume 10, Issue 108, Page 7472

Authors

Isaac Kinley
Postdoctoral Fellow, Rotman Research Institute, Canada

Editor

Sehrish Kanwal

Tags

psychology economics behaviour decision making temporal discounting delay discounting intertemporal choice

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Citation (CITATION.cff)

cff-version: "1.2.0"
authors:
- family-names: Kinley
  given-names: Isaac
  orcid: "https://orcid.org/0000-0003-2057-9606"
contact:
- family-names: Kinley
  given-names: Isaac
  orcid: "https://orcid.org/0000-0003-2057-9606"
doi: 10.5281/zenodo.15076623
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Kinley
    given-names: Isaac
    orcid: "https://orcid.org/0000-0003-2057-9606"
  date-published: 2025-04-08
  doi: 10.21105/joss.07472
  issn: 2475-9066
  issue: 108
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 7472
  title: "tempodisco: an R package for temporal discounting"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.07472"
  volume: 10
title: "tempodisco: an R package for temporal discounting"

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