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discord

discord is an R package that provides functions for discordant kinship modeling and other sibling-based quasi-experimental designs. It includes functions for data preparation, regression analysis, and simulation of genetically-informed data. The package is designed to facilitate the implementation of discordant sibling designs in research, allowing for the control of shared familial confounding factors.

Visit the discord website for more information and detailed documentation. Below is a brief overview of the package, its features, and vignettes to get you started.

Features

  • Data Preparation: Functions to prepare and structure data for discordant sibling analysis, including handling of kinship pairs and demographic variables.
  • Regression Analysis: Tools to perform discordant regression analyses, allowing for the examination of within-family effects while controlling for shared familial confounders.
  • Simulation: Functions to simulate genetically-informed data, enabling researchers to test and validate their models.

Vignettes

The package includes several vignettes to help users understand and utilize its features effectively. These vignettes can be accessed online or by selecting them from the RStudio “Vignettes” tab after installing the package.

The following vignettes are available:

  • Power Analysis with discord
    • Use this vignette when you need to plan sample sizes or evaluate detectability by running simulation grids that vary effect sizes, kin types, and Ns using kinsim, then re-fitting discord_regression under each condition. It reports empirical power, writes tidy summaries, and includes code to visualize power curves across conditions to support design decisions.
  • Regression Analysis with discord
    • Use this if you want an end-to-end applied example that links NLSY79 relatives, cleans variables for flu vaccination and SES, constructs dyads, and then fits within-family models. You will learn how to specify discord_regression correctly and interpret coefficients when predictors vary within pairs versus only between pairs.
  • Handling Categorical Predictors
    • This vignette formalizes categorical predictors in discord designs by separating mixed from between-dyad variables and making the implied contrasts explicit. It implements binary-match and multi-category match encodings on concrete examples (e.g., sex, race), fits the corresponding discord_regression specifications, and contrasts estimates to show how encoding choices change interpretation.
  • Full Data Workflow
    • Starting from raw wide and long person-level inputs, this vignette builds kin links, aligns IDs, and constructs the discord_data object with sibling-specific columns ready for modeling. It then fits a conventional between-family regression alongside a discord model on the same variables, so you can see where the within-family estimate diverges and adopt the pipeline as a template.
  • Creating Plots
    • This vignette takes fitted discord_regression outputs and produces publication-ready ggplot figures of effect estimates and within-family contrasts with minimal transformation of the model results. It includes complete plotting code paths you can reuse, from extracting estimates to saving figures that clearly communicate within-family findings.
  • No Database? No Problem: Using discord with Simple Family Structures
    • This vignette is particularly useful for situations when you do not have existing kinship links and need to build link tables directly from simple family identifiers. It shows how to construct the links, optionally simulate phenotypes under specified structures, and fit discord_regression with alternative specifications for small or bespoke datasets.

External Reproducible Examples

Beyond the vignettes, you can find additional examples that fully reproduce analyses from our other publications (Garrison et al 2025, etc). These examples can be accessed via the following links:

  • National Longitudinal Survey of Youth (NLSY) dataset

    • Intelligence: Reproduces Garrison, S. M., & Rodgers, J. L. (2016). Casting doubt on the causal link between intelligence and age at first intercourse: A cross-generational sibling comparison design using the NLSY. Intelligence, 59, 139-156. https://doi.org/10.1016/j.intell.2016.08.008

    • Frontiers: Reproduces Sims, E. E., Trattner, J. D., & Garrison, S. M. (2024). Exploring the relationship between depression and delinquency: a sibling comparison design using the NLSY. Frontiers in psychology, 15, 1430978. https://doi.org/10.3389/fpsyg.2024.1430978

    • AMPPS: Reproduces analyses from Garrison et al 2025, using targets for workflow management. Garrison, S. M., Trattner, J. D., Lyu, X., Prillaman, H. R., McKinzie, L., Thompson, S. H. E., & Rodgers, J. L. (2025). Sibling Models Can Test Causal Claims without Experiments: Applications for Psychology. https://doi.org/10.1101/2025.08.25.25334395

  • China Family Panel Studies (CFPS) dataset
    • AMPPS: Reproduces analyses from the China Family Panel Studies (CFPS) dataset, focusing on the association between adolescent depression and math achievement.

Installation

You can install the official version from CRAN

# Install/update discord with the release version from CRAN.
install.packages('discord')

You can also install/update discord with the development version of discord from GitHub with:

# If devtools is not installed, uncomment the line below.
# install.packages('devtools')
devtools::install_github('R-Computing-Lab/discord')

Citation

If you use discord in your research or wish to refer to it, please cite the following paper:

citation(package = "discord")
To cite package 'discord' in publications use:

  Garrison S, Trattner J, Hwang Y (????). _discord: Functions for
  Discordant Kinship Modeling_. R package version 1.2.4.1,
  <https://github.com/R-Computing-Lab/discord>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {discord: Functions for Discordant Kinship Modeling},
    author = {S. Mason Garrison and Jonathan Trattner and Yoo Ri Hwang},
    note = {R package version 1.2.4.1},
    url = {https://github.com/R-Computing-Lab/discord},
  }

Contributing

Contributions to the discord project are welcome. For guidelines on how to contribute, please refer to the Contributing Guidelines. Issues and pull requests should be submitted on the GitHub repository. For support, please use the GitHub issues page.

License

discord is licensed under the GNU General Public License v3.0. For more details, see the LICENSE file.