Steven J. Pierce
This package (Pierce, 2026) is a research compendium (Marwick et al., 2018) for a study examining attrition from a sexual assault nurse examiner (SANE) training program (Campbell et al., 2026). The research team is led by Dr. Katherine Dontje (PI) and Dr. Rebecca Campbell (Co-I) at Michigan State University and was supported by a grant (Dontje & Campbell, 07/01/2021–06/30/2025). The training program had two major components: didactic training (DT) followed by a clinical skills workshop (CSW).
The study addresses the following research questions:
- RQ1. What are the eligibility and enrollment rates for this program, and do eligible vs. enrolled participants differ with respect to their background, motivations, potential barriers, and emotional readiness for this kind of work?
- RQ2. What are the attrition rates from enrollment to completion of the CSW, and when (i.e., at which module[s]) did participants commonly attrit?
- RQ3. Do participants’ background characteristics, motivations for seeking training, potential barriers, and emotional readiness for this work predict attrition rates?
- RQ4. For the participants who completed DT and those who completed the CSW, to what extent did they find each training component to be useful/helpful?
We eventually expect two different types of users of this package: collaborators and readers. Collaborators include members of the research team working on the project prior to publication who may contribute to the code. Readers include members of the public who simply want to reproduce the computations and analyses after reading the paper. These instructions include some steps that are more relevant to collaborators than to readers.
To collaborate on the code for this package, you must have a GitHub account. You can request such access by emailing the package maintainer your GitHub username and ask to be added as a collaborator on this repository. You will need to use Git (Torvalds et al., 2026) for version control on files associated with this package and to synchronize changes between your local copy of the repository and GitHub, with RStudio (RStudio Team, 2026) as the primary editor. There is a lot of useful information about using these tools at the Happy Git and GitHub for the userR website (Bryan et al., n.d.). Other useful resources on using Git and GitHub include Bryan (2018) and Perez-Riverol et al. (2016). Meanwhile, Wickham & Bryan (2021) provides extensive guidance on creating R packages. Chacon & Straub (2014) is a full book on using Git for version control.
Changes made to files in a local copy of the repository should be committed and pushed to the main branch of the remote SANETPA repository on GitHub. See Bryan (2018) for a short introduction to why this is good practice.
We assume you are using the most recent stable release versions of the software packages discussed below and also frequently updating your installed R packages.
This package is only available from a public repository available on the GitHub server at https://github.com/sjpierce/SANETPA. Public repositories are visible to everyone but can only be edited by GitHub users who are logged in and have been registered by the repository owner as a collaborator on the project.
This package’s repository was private until the associated manuscript was accepted for publication. After that, we made it public to enable readers to reproduce the analyses.
The package can be installed with the information shown below. The overall goal here is to set you up for using a suite of software tools and practices that works well for reproducible research. That will facilitate using this package.
You should create a GitHub user account before proceeding further. After you have the GitHub account, send the SANETPA package maintainer your GitHub username and ask to be added as a collaborator on the repository. This is necessary because the main branch of the package repository is stored on GitHub. You will need to be able to use Git and GitHub to synchronize changes between your local copy of the repository and GitHub.
You can get the most recent version of R (R Development Core Team, 2025) from the Comprehensive R Archive Network (CRAN).
Install any tools required for compiling packages (they will be specific to your operating system). These will be necessary for the devtools package to work.
- On Windows, see https://cran.r-project.org/bin/windows/Rtools/.
- On Mac OS X, see https://cran.r-project.org/bin/macosx/tools and https://mac.R-project.org/tools/.
Install RStudio Desktop version 2026.01.0+392 (or later). We recommend using RStudio to interact with the files for this package. RStudio is both a good interface to R and has built-in support for using some of the other software discussed below.
We rely on Quarto (Allaire et al., 2025) scripts to enhance reproducibility because they provide excellent support for generating dynamic reports (Mair, 2016). Install Quarto version 1.8.27 or later. Although RStudio bundles a version of Quarto, we want the most recent stable release instead. Quarto also includes a copy of Pandoc.
- To download Quarto, visit https://quarto.org/docs/get-started/
This step is required for collaborators, but optional for readers. Readers can just skip to the “Install devtools package section”.
Install Git. We use this for version control on the package code. Get the most recent version available for your operating system. See instructions at https://happygitwithr.com/install-git.html.
- On Windows, download from https://git-scm.com/download/win
- On MacOS, download from https://git-scm.com/download/mac
This step is required for collaborators, but optional for readers. Readers can just skip to the “Install devtools package” section.
Configure Git using the instructions at https://happygitwithr.com/hello-git.html. RStudio can be your main interface to the Git client most of the time, but occasionally using a Git Bash command window instead is more useful. You can open that by clicking Start > All Programs > Git > Git Bash on your own computer.
You will need to configure RStudio to use Git and GitHub. Use the instructions at https://happygitwithr.com/connect-intro.html. The reason for that is that because RStudio is both a good interface to R and has built-in support for Git.
Read Section 9 of the Happy Git with R website (https://happygitwithr.com/https-pat.html) to learn more about what a PAT is, how to get one, and why it is useful. The following subsections may be particularly useful.
- https://happygitwithr.com/https-pat.html#valid-pat-gets-stored-but-later-told-the-pat-is-invalid
- https://happygitwithr.com/https-pat.html#pat-doesnt-persist-on-macos-or-windows
- https://happygitwithr.com/https-pat.html#pat-doesnt-persist-on-linux
You may also want to read GitHub documentation about managing PATs (https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens).
Visit https://github.com/settings/tokens to create a PAT that has the
gist, repo, user, and workflow scopes. Once you have it, you’ll
need to store it so your computer can find your PAT automatically.
We want to store your PAT in the Git Credential Manager because that allows RStudio and Git to easily connect to GitHub for pushing and pulling commits.
We can use the credentials package for R to facilitate this task.
# If you don't already have it installed.
install.packages("credentials")Then, run the following function call.
credentials::git_credential_ask("https://github.com")If this prompts you for username and password, use the PAT as the password. If that just displays a result but the password does not match your PAT, run the code below then enter your GitHub username and use your PAT as the password.
credentials::git_credential_update("https://github.com")Install the devtools package for R. The most recent CRAN release will likely be more stable but sometimes you may instead need the development version at GitHub. This package provides developer tools/functions that simplify creating, quality-checking, and installing custom R packages. You can use the following command at the R console.
install.packages("devtools")Install the piercer package for R. Instructions for doing that are at the link. Please read and follow them before trying to install or use this package.
TinyTex is a specific distribution of
LaTeX, which is document preparation software that allows high-quality
typesetting. It takes plain text LaTeX files (*.tex files) that
describe the structure of a document and compiles them into
fully-formatted PDF files with nice fonts and layout. We can actually
use the R package called
tinytex to install TinyTeX
via the following commands inside R.
# If you don't already have it installed.
install.packages("tinytex")tinytex::install_tinytex()You can use alternative LaTeX distributions and tools (e.g., MiKTeX) instead, but TinyTeX is very convenient because of how well it integrates with the other tools we’re using.
Major versions of TinyTex are released at least annually. If it has been a long time since you last installed TinyTeX, you may want to update it using the code below. This should refresh both the base TinyTex and the LaTeX packages you have previously installed by using it.
tinytex::reinstall_tinytex()It is a good idea to update all your R packages. You may be prompted with a dialog box asking “Do you want to install from sources the packages which need compilation?” It usually works fine if I choose “no”. Occasionally, it appears necessary to choose “yes”, but I am more likely to run into problems when doing that.
update.packages(ask='graphics', checkBuilt=TRUE)If you previously were using an older version of R (any version before
4.5.0), you should plan to reinstall all your R packages from scratch.
The best way to do that is to use a script such as
scripts/Reinstall_Packages.R under the older version to save a data
file containing the names of installed packages, then remove the older
version of R and replace it with the newest version of R, and use the
remainder of that script to read in that list of packages and install
them. That will take several minutes if you have a lot of packages.
Once the repository is made public, readers should be able to download a
ZIP file containing the latest released version of the repository
contents from https://github.com/sjpierce/SANETPA/releases/latest. Just
unzip that to create a folder on your local computer such as
C:\Users\username\Documents\SANETPA; that will be your local copy of
the repository.
If you want to work with the package on your laptop, use RStudio to
clone the SANETPA package from GitHub (the repository source is
https://github.com/sjpierce/SANETPA.git) to a local folder on your
computer such as C:\Users\username\Documents\SANETPA. This is the
folder where you would edit scripts and files and that you would
synchronize with the GitHub main branch via Git’s pull and push
operations.
Ideally, only one person should be using a given local repository at a time. The beauty of Git is that it allows us all synchronize with the main repository on GitHub regardless of where our local copies are stored.
You have to install the package to your personal R package library
before some of the scripts will work because they may depend on
functions defined in the package. This personal R package library would
usually be in a location such as
C:\Users\username\AppData\Local\R\win-library\4.5\SANETPA on your
laptop, desktop, or on the server. Note that this is distinct from the
local repository folder!
Scripts that do not have a library(SANETPA) call in them may work
without the package being installed, but those containing that call
depend on custom functions found only in the SANETPA package. When you
use that call, R loads the copy of the package found in your personal R
package library, not from the local Git repository.
Now you should be ready to actually install the package. There are two main ways to do that: from GitHub and from the local repository. Both are explained below.
You should be ready to actually install the package from the main branch on GitHub. This is the recommended default method for installing SANETPA.
devtools::install_github(repo = "sjpierce/SANETPA", dependencies = TRUE)If you can use Git pull and push successfully but the installation command above does not work, the problem may be that you need to store your Git credentials in the Git credential manager or to update them there.
You may sometimes want to build and install the package from the local repository directly to your personal R package library instead of pulling the copy from GitHub. This can be useful when testing new code before you commit it to the main branch or if you do not use Git and GitHub.
Double-click the SANETPA.Rproj file from Windows Explorer. That should
open the project in RStudio. Then run the following code in a fresh R
session.
library(devtools)
document()
check()
install()The structure for the package is shown in the outline below, where
folder names and file names are highlighted like this and comments are
in normal text. The folder structure is largely determined by the
conventions governing the structure of R packages. It deviates a bit
from the example research compendium folder structures discussed by
Marwick et al. (2018). The repository is also set up as an RStudio
project.
SANETPA/: This is the root folder for the repository..git/: This hidden folder is used by Git. Leave it alone!.Rproj.user/: This hidden folder is used by Rstudio. Leave it alone!data/: This folder is where the.RDatafile produced by our scripts will be stored. This is a standard folder for R package structures.Imported_SANETP_Data.RData: This file is created by rendering thescripts/Import_Data.qmdscript. Re-running the script will overwrite it.Placeholder.textThis text file is just present to ensure that thedatasubfolder will be created when you clone the repository or extract files from ZIP file copy of the repository obtained from GitHub.
graphics/: This folder stores.eps,.pdf, and.pnggraphics files for CSTAT and MSU logos and/or wordmarks used in my scripts for branding purposes.man/: This folder contains R documentation help files (*.Rd) for the package and its custom functions. It is required by R package building conventions. You should not edit these files manually and you really only access them through R’s normal help system.R/: This folder contains the source code for the package’s custom functions in a set of*.Rscript files. It is required by R package building conventions.SANETPA-package.R: This script file is used to automate creating package level help files. Do not edit it manually.
scripts/The folder is configured as a Quarto project. It holds meta-data files,.qmdscripts, and files used by the scripts..quarto/: This hidden folder may be created by Quarto to hold temporary files. Do not edit or delete any of these files unless you know what you are doing! This folder is not tracked by Git.extdata/: This subfolder contains raw data used by my scripts.All_group_1_2025-11-19.sav: This SPSS data file holds all the de-identified raw data.
output/: This subfolder holds rendered output files.Campbell_Figure_1_yyyy-mm-dd.png: This image file is a temporary file that gets converted to a.tiffile. It is created by thescripts/Export_Diagram.qmdscript.Campbell_Figure_1_yyyy-mm-dd.tif: This image file is suitable for submission to a journal. It is created by thescripts/Export_Diagram.qmdscript.Campbell_Figure_2_yyyy-mm-dd.tif: This image file is suitable for submission to a journal. It is created by thescripts/RQ2_RQ3_Analyses.qmdscript.Descriptive_Analyses_Draft.pdf: This is temporary draft output obtained by rendering thescripts/Descriptive_Analyses.qmdscript directly.Descriptive_Analyses_yyyy-mm-dd.pdf: Production output obtained by usingscripts/Production_Run.qmdto render thescripts/Descriptive_Analyses.qmdscript will have names following this date-stamped naming convention. An actual rendering date will replace theyyyy-mm-ddnotation.Import_Data_Draft.pdf: This is temporary draft output obtained by rendering thescripts/Import_Data.qmdscript directly.Import_Data_yyyy-mm-dd.pdf: Production output obtained by usingscripts/Production_Run.qmdto render thescripts/Import_Data.qmdscript will have names following this date-stamped naming convention. An actual rendering date will replace theyyyy-mm-ddnotation.RQ1_Analyses_Draft.pdf: This is temporary draft output obtained by rendering thescripts/RQ1_Analyses.qmdscript directly.RQ1_Analyses_yyyy-mm-dd.pdf: Production output obtained by usingscripts/Production_Run.qmdto render thescripts/RQ1_Analyses.qmdscript will have names following this date-stamped naming convention. An actual rendering date will replace theyyyy-mm-ddnotation.RQ2_RQ3_Analyses_Draft.pdf: This is temporary draft output obtained by rendering thescripts/RQ2_RQ3_Analyses.qmdscript directly.RQ2_RQ3_Analyses_yyyy-mm-dd.pdf: Production output obtained by usingscripts/Production_Run.qmdto render thescripts/RQ2_RQ3_Analyses.qmdscript will have names following this date-stamped naming convention. An actual rendering date will replace theyyyy-mm-ddnotation.RQ4_Analyses_Draft.pdf: This is temporary draft output obtained by rendering thescripts/RQ4_Analyses.qmdscript directly.RQ4_Analyses_yyyy-mm-dd.pdf: Production output obtained by usingscripts/Production_Run.qmdto render thescripts/RQ4_Analyses.qmdscript will have names following this date-stamped naming convention. An actual rendering date will replace theyyyy-mm-ddnotation.Production_Run.html: This is the log resulting from renderingscripts/Production_Run.qmddirectly with the Render button. This documents when the production process was run and the result of each step.Production_Run_yyyy-mm-dd.html: This is the log resulting from renderingscripts/Production_Run.qmdwith a Terminal command as shown below in the Reproducing Our Results section of this document. It documents when the production process was run and the result of each step. An actual rendering date will replace theyyyy-mm-ddnotation.
.gitignore: This was auto-created by Quarto. Don’t edit or delete it..quarto/: This hidden folder may be created by Quarto to hold temporary files. Do not edit or delete any of these files unless you know what you are doing! This folder is not tracked by Git._brand.yml: This file specifies color, font, and logo settings for using an MSU/CSTAT branding scheme for HTML output._quarto.yml: This is a Quarto metadata file containing project-level YAML code that will be inherited by Quarto scripts in this folder or its subfolders.apa.csl: This is a citation style language file for the Publication Manual of the American Psychological Association, 7th ed. It is used by Quarto to format reference sections.compact-title.tex: This LaTeX file can be called from an .qmd file’s YAML header via the in_header option. It just formats the title of the rendered PDF file so it uses less vertical white space. It is used by multiple other scripts.CSTAT_style.css: This custom style sheet provides logo and title banner formatting settings and classes for my HTML outputs. It supplements_brand.ymlandCSTAT_theme.scss.CSTAT_theme.scss: This is a Sassy custom style sheet file to provide formatting settings and classes for my slides and other HTML outputs. It supplements_brand.ymlandCSTAT_style.css.Delete_nul_file.bat: This is a Windows batch file that automates removing a nuisance file sometimes left over when rendering a Quarto or R Markdown script doesn’t work right.Descriptive_Analyses.qmd: This script runs some supplemental analyses that don;t directly address the research questions. Methodology notes, references, and results interpretation are built into this script and its output. Rendering this directly generates draft output namedoutput/Descriptive_Analyses_Draft.pdf; you can get date-stamped production output (e.g.,output/Descriptive_Analyses_yyyy-mm-dd.pdf) by usingscripts/Production_Run.qmdto render this script.Development_Tools.R: This contains some examples of R commands I use interactively when working on the package.Export_Diagram.qmd: This script runs exports Figure 1 for the . manuscript. Rendering this directly generates draft output namedoutput/Export_Diagram_Draft.pdf; you can get date-stamped production output (e.g.,output/Export_Diagram_yyyy-mm-dd.pdf) by usingscripts/Production_Run.qmdto render this script.Import_Data.qmd: This script imports the raw data from SPSS and saves it in an.RDatafile for use by other scripts here. Rendering this directly generates draft output namedoutput/Import_Data_Draft.pdf; you can get date-stamped production output (e.g.,output/Import_Data_yyyy-mm-dd.pdf) by usingscripts/Production_Run.qmdto render this script.Production_Run.qmd: This script is used to automate rendering other scripts in the proper order to generate date-stamped production outputs (e.g.,output/Import_Data_yyyy-mm-dd.pdf). It also producesscripts/output/Production_Run.html.references.bib: This is a BibTeX file containing the citation data for references mentioned in various scripts. Quarto uses it to get the data needed to insert reference lists.Reinstall_Packages.R: This script contains R code that stores a data file containing a list of all your installed packages, plus code for reading that file and re-installing all of those packages from scratch. It automates an otherwise tedious process. You would use this before and after upgrading to a new version of R (e.g., when going from version 4.4.x to 4.5.x).RQ1_Analyses.qmd: This script runs the analyses for Research Question 1. Methodology notes, references, and results interpretation are built into this script and its output. Rendering this directly generates draft output namedoutput/RQ1_Analyses_Draft.pdf; you can get date-stamped production output (e.g.,output/RQ1_Analyses_yyyy-mm-dd.pdf) by usingscripts/Production_Run.qmdto render this script.RQ2_RQ3_Analyses.qmd: This script runs the stopping-ratio model(s) that comprise the analyses for Research Questions 2 and 3. Methodology notes, references, and results interpretation are built into this script and its output. Rendering this directly generates draft output namedoutput/RQ2_RQ3_Analyses_Draft.pdf; you can get date-stamped production output (e.g.,output/RQ2_RQ3_Analyses_yyyy-mm-dd.pdf) by usingscripts/Production_Run.qmdto render this script.RQ4_Analyses.qmd: This script runs the analyses for Research Question 4. Methodology notes, references, and results interpretation are built into this script and its output. Rendering this directly generates draft output namedoutput/RQ4_Analyses_Draft.pdf; you can get date-stamped production output (e.g.,output/RQ4_Analyses_yyyy-mm-dd.pdf) by usingscripts/Production_Run.qmdto render this script.Setup_as_Package.qmd: This is a script I used to remind myself of how to rapidly do various parts of turning a new repository into an R package. It’s only really used once.title.tex: This LaTeX file enables better handling of author affiliations in PDF outputs. It is used by multiple other scripts.
.gitignore: This file tells Git what files to ignore and omit from synchronizing with the main repository on GitHub..Rbuildignore: This file tells R what files to ignore when building the package from the source code.DESCRIPTION: This file is a brief, structured description of the package that is required by R package building conventions. It holds essential meta-data.LICENSE: This file contains the terms of the license that applies to all content other than source code in this repository.LICENSE.md: This file contains the terms of the license that applies to all source code in this repository.LICENSE.note: This file describes which license applies to different kinds of content in this repository.NAMESPACE: This file is created automatically by R when building the package. You should not edit it manually. It is required by R package building conventions.NEWS.md: This file contains an list of comments about the changes made with each version of this package. It is required by R package building conventionsREADME.md: This file is obtained by rendering theREADME.qmdfile and is used by GitHub to display information about the package. Do not edit it manually: just renderREADME.qmdto update it. In R Studio, you can read the formatted version by opening the file and clicking the Preview button.README.qmd: This file gives an introduction to the package. Rendering it produces theREADME.mdfile and opens the preview automatically.SANETPA.Rproj: This is an RStudio project file. It contains some settings for working with the project in that software.
Scripts in this R package may depend on having a number of other R
packages installed. Those packages are listed in the DESCRIPTION
file’s Depends, Suggests, and Imports fields. They are mostly available
from CRAN and should be installed automatically when you install the
SANETPA package itself if you use dependencies = TRUE option.
Many software packages are updated periodically, so it is a good idea to update to the newest stable version of them occasionally. Most of us accumulate a large number of installed R packages. There is a good chance that at least one of them will have been updated every week, so updating R packages regularly is a good idea. User-contributed packages collectively change more often than base R.
Staying reasonably current on software versions for the whole suite of tools we are using here will keep things working smoothly most of the time. It also helps if we are using the same versions wherever possible because version differences can introduce discrepancies in the results we each obtain.
The final raw data required to use this package are available in the
GitHub repository’s scripts/extdata/ folder. They have been
de-identified to protect participant privacy and confidentiality.
The SPSS data file script/extdata/All_group_1_2025-11-19.sav has
variable and value labels defined. The scripts/Import_Data.qmd script
reads that file into R, produces a list of all the variables in the raw
data, does a fair bit of data management, then writes out the
data/Imported_SANETP_Data.RData file that is used by the analysis
scripts. That imported data file contains multiple R data frames derived
from the raw data. Read the output of the script that produces it to see
exactly what happens with the data and get additional information about
the data.
The file data/Imported_SANETP_Data.RData included in the repository is
the result I obtained with the final production run of the import
script.
After it has been installed to your package library as described above, you can load SANETPA via the following R console command. That provides access to the custom R functions we have included in the package.
library(SANETPA)Loading the package will also mean that we can use functions like
devtools::session_info() to show the package version number in our
output. That facilitates reproducibility by making it easier to see the
software environment required to obtain a particular result.
You can see information about the package by using the following command in the R console. The resulting help page has an Index link at the bottom that will show you a list of all the custom functions in the package.
?SANETPAOne of the main uses of the package is to run scripts that import,
manage, and analyze data for the manuscript it supports. If you want to
reproduce the results, you can double-click the SANETPA.Rproj file
from Windows Explorer to open the project in RStudio. Rendering a Quarto
script:
- May read external data files from the
scripts/extdata/folder. - May write R data files to the
data/folder. - Will generate output files in the
scripts/output/folder.
Scripts in this package can generate either draft or production output. I wrote a blog post describing a rationale for, and approach to, separating draft and production output. That approach has been implemented in this compendium.
You can get draft output by rendering a script with its default
parameters. Just use RStudio to open the relevant Quarto script (e.g.,
scripts/Import_Data.qmd) and then click the “Render” button in
RStudio. This is most useful to collaborators on our research team while
we’re developing the compendium before publication of the associated
paper. This allows us to focus on one script at a time. We tend to edit
and render draft output frequently as we do so, overwriting the draft
output each time to avoid creating too much file clutter.
Our final results for publication will be generated as production
output. The production process requires rendering each of the main
scripts in a specific order. We automated that process via the
scripts/Production_Run.qmd script. Use RStudio to open that file and
then click the “Render” button. If you have everything set up correctly,
that will create date-stamped output files in the scripts/output/
folder, plus the file scripts/output/Production_Run.html. The latter
file contains a data flow diagram showing all the inputs and outputs of
the production process, how long it took to render each script called by
the production script, and information about the software environment
used.
If you want to get a date-stamped output for the production run script
itself, you can use something like the following command in the Terminal
to change into the scripts/ folder and render that output. Just update
the date in the output filename to match the current date (a new output
file will overwrite an existing output file if they have the same file
name).
cd scripts
quarto render Production_Run.qmd --output Production_Run_2026-02-14.html
Allaire, J. J., Dervieux, C., Scheidegger, C., Teague, C., & Xie, Y. (2025). Quarto (Version 1.8.27) [Computer Program]. Posit Software, PBC. https://quarto.org
Bryan, J. (2018). Excuse me, do you have a moment to talk about version control? The American Statistician, 72(1), 20–27. https://doi.org/10.1080/00031305.2017.1399928
Bryan, J., TAs, T. S. 545., & Hester, J. (n.d.). Happy Git and GitHub for the useR [Web Page]. https://happygitwithr.com
Campbell, R., Pierce, S. J., Ashley, A., Markowitz, J., & Dontje, K. (2026). Predicting trainee attrition rates in a state-wide sexual assault nurse examiner (SANE) didactic and clinical training program [Manuscript submitted for publication]. Department of Psychology, Michigan State University.
Chacon, S., & Straub, B. (2014). Pro Git. Apress Media. https://git-scm.com/book/en/v2
Dontje, K., & Campbell, R. (07/01/2021–06/30/2025). Increasing access, recruitment, and retention of sexual assault nurse examiners in rural michigan (Grant No. T96HP42059). Health Resources and Services Administration.
Mair, P. (2016). Thou shalt be reproducible! A technology perspective. Frontiers in Psychology, 7(1079), 1–17. https://doi.org/10.3389/fpsyg.2016.01079
Marwick, B., Boettiger, C., & Mullen, L. (2018). Packaging data analytical work reproducibly using R (and friends). The American Statistician, 72(1), 80–88. https://doi.org/10.1080/00031305.2017.1375986
Perez-Riverol, Y., Gatto, L., Wang, R., Sachsenberg, T., Uszkoreit, J., Leprevost, F. da V., Fufezan, C., Ternent, T., Eglen, S. J., Katz, D. S., Pollard, T. J., Konovalov, A., Flight, R. M., Blin, K., & Vizcaíno, J. A. (2016). Ten simple rules for taking advantage of Git and GitHub. PLOS Computational Biology, 12(7), e1004947. https://doi.org/10.1371/journal.pcbi.1004947
Pierce, S. J. (2026). SANETPA: Research compendium for a study of sexual assault nurse examiner training program attrition (Version 1.0.1) [Reproducible Research Materials and Computer Program, R Package, Public Repository]. GitHub. https://github.com/sjpierce/SANETPA. Zenodo. https://doi.org/10.5281/zenodo.18643254
R Development Core Team. (2025). R: A language and environment for statistical computing (Version 4.5.2) [Computer Program]. R Foundation for Statistical Computing. http://www.R-project.org
RStudio Team. (2026). RStudio Desktop: Integrated development environment for R (Version 2026.01.0+392) [Computer Program]. Posit Software, PBC. https://posit.co
Torvalds, L., Hamano, J. C., & other contributors to the Git Project. (2026). Git for Windows (Version 2.53.0(1)) [Computer Program]. Software Freedom Conservancy. https://git-scm.com
Wickham, H., & Bryan, J. (2021). R packages: Organize, test, document, and share your code. O’Reilly Media. https://r-pkgs.org
Please cite the package itself (Pierce, 2026), which includes the necessary data.
The opinions or points of view expressed in this document (or any other document included in this R package and repository) are solely those of the authors and do not reflect the official positions of any organization.