This repository contains materials and code for our manuscript "Perceptual Disfluency and Recognition Memory: A Response Time Distributional Analysis". The data, materials, and model files can be found here: https://osf.io/6sy7k/.

• .Rprofile: Configuration file for R sessions.
• .gitignore: Specifies files and directories for Git to ignore.
• Disfluency_ms.Rproj: RStudio project file.
• README.md: Provides an overview of the project.
• create_env_dev.R: Script to set up the nix environment
• default.nix: Configuration file for the Nix package manager.
• .project: Project configuration file.
• exguass_reparm.R: correct brms family to get mean of guassian and not mean of dist
• grateful-report.html: R packages used in paper citations

• _extensions/: Contains extensions, including:

  • wjschne/apaquarto/

• _manuscript/: Manuscript-related files.

• All data for this manuscript can be found in the OSF repo. Large amounts of data and size of model obects is far too large to store on Github.

This repository contains all the resources needed to reproduce the manuscript associated with this project. To ensure maximum reproducibility, we used Quarto for creating the manuscript. This allows computational figures, tables, and text to be programmatically included directly in the manuscript, ensuring that all results are seamlessly integrated into the document. We also provide a file called default.nix which contains the definition of the development environment that was used to work on the analysis. Reproducers can easily re-use the exact same environment by installing the Nix package manager and using the included default.nix file to set up the right environment.

Here is a video tutorial showing an example of how to reproduce a manuscript using Nix/Rix

To reproduce the manuscript, you will need the following if not using rix/nix:

1. Git - To get Github repos [https://git-scm.com/downloads]
2. RStudio or Positron or VS Code- To run the R scripts and render the Quarto document.
3. Quarto - To compile the manuscript.
4. apaQuarto - APA manuscript template [https://github.com/wjschne/apaquarto/tree/main] (you should not have to download this if you download the repo as the _extension file contains all the files needed)

• Nix and Rix
  • For Windows and Linux: Setup Guide
  • For macOS: Setup Guide

Clone this repository to your local machine:

git clone https://github.com/jgeller112/Disfluency_Ms.git
cd Disfluency_Ms

• You can also clone the repository from Github using the SSH and opeining a project in RStudio/Positron.

Open the R project file Disfluency_Ms.Rproj in RStudio or Positron.

Use Nix to set up the reproducible environment:

nix-build

nix-shell

Once in the shell, You can:

1. Reproduce the manuscript

quarto render "~/_manuscript/Disfluency_Modeling_Ms.qmd"

or

2. Launch your IDE in the correct environment in run code and analyses:

• Positron
  • To use Positron from the shell you will need to make sure the correct path is set (see posit-dev/positron#4485 (comment)). Once this is done you can open Positron from the shell

positron

For RStudio, simply type:

rstudio

Finally, it’s also possible to forget {rix} and instead run everything using R packages that you install systemwide.

• Make sure the required software is installed above and you have the following packages:

  • R 4.4.1 (or later) and RStudio.

  • Quarto 1.6.1 (or later)

  • A C++ compiler and GNU Make. Complete instructions for macOS, Windows, and Linux are available at CmdStan’s documentation. In short, do this:

    • macOS: Run this terminal command and follow the dialog that pops up after to install macOS’s Command Line Tools:

  xcode-select --install

Windows: Download and install Rtools from CRAN

Linux: Run this terminal command (depending on your distribution; this assumes Ubuntu/Debian):

sudo apt install g++ make
(macOS only): Download and install XQuartz

install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev", "https://packagemanager.posit.co/cran/latest"))

# install cmdstan 
cmdstanr::install_cmdstan()

1. Download the repository from Github

2. Open Disfluency_Ms.Rproj to open a new RStudio project.

3. Open /_manuscript/Disfluency_Modeling_Ms.qmd

4. Run each chunk in the manuscript

Note that some computations can take a long time, depending on computer performance etc