Because my research projects have lead me to create a wide variety of tools for R and Python, I decided to package them in statistical software to facilitate the usage of code across projects. After finishing up the dissertation, I intend to wrap up several tool sets to aid other scholars and practitioners in their work.


Although it is important to commit to a specific empirical process (i.e., measurement and analysis) to avoid p-hacking and other problematic practices, one’s evolving understanding of the analysis, context, etc. could lead the researcher to explore alternative measurements or model specifications. To give the reader openness about the different empirical choices that were made, it is therefore important to also be open about what alternatives were not shown in the main tables. With this R package, I provide helpful functions and visualizations that provide information about the ‘multiverse’ of possibilities, and how the results would (comparatively) look like.

Example functions include:

Example visualizations include:


For my research, I had to create several specific functions that I bundled in a single R package. Functions include:


Similarly, I created a Python library:

Other resources

There are also several other research that I have created (or modified) that have been useful to me: