We are actively engaged in data analysis projects for members of the local Winston-Salem community. Currently, our focus is on data from the Reynolda House. We are helping the Reynolda House manage, analyse, and visualize their data.
We are working to simplify sensitivity to unmeasured confounding analyses so that they can be implemented by non-statistical researchers in clinical or social science settings. We have created an R package on CRAN, tipr, to facilitate this.
In the spirit of studying human data interaction, we are developing an R package that can be used to analyze R code in the tidyverse paradigm.
We are developing shiny applications for the classroom setting.
We are actively examining best practices in teaching data science, both in the classroom and via Massive Open Online Courses (MOOCs).
We are applying causal inference techniques to augment clinical trial data with observational data.