R

This is a Shiny app to demonstrate to students how much randomly assigned groups can differ on some measure without it actually being a significant difference.

This is an interactive dashboard created to visualize the spread of COVID-19 in the state of South Carolina. It updates daily.

This is an interactive data visualization created to look at geographic variation in polling error for recent US elections.

This is an R package that contains tools for the management and analysis of panel data. The main contributions are a panel_data object class designed to make panel-specific functions easier to handle and wbm, a procedure for fitting within-between regression models.

Previously part of the jtools package, this provides a set of functions that aid the analysis of statistical interactions. It implement simple slopes analysis, the calculation of Johnson-Neyman intervals, and plots for understanding interaction effects.

An expanding collection of tools I originally created to aid in my own research. The unifying theme is the ability to report and visualize the results of regression models. Most popular are functions that provide a streamlined, customizable summary of regressions (including robust standard error support) in the console, HTML/LaTeX/Word tables, and coefficient plots. A few other tools have been described in my blog and elsewhere.

Here I discuss statistical and logistical details involved in my attempts at modeling kicker talent. This includes model selection as well as a brief history of research. I use some quantitative assessments to compare my approach to previous attempts and it does quite well.

Observers note that in the 2020 Democratic primary, candidate support in polls is closely correlated with the amount of attention candidates get in the news. This begs the question of whether and how these two things might be causally related, particularly whether news coverage is helpful to candidates. Here I show some evidence that news coverage does indeed help.

It has been just a little more than a day since I announced my new R package, panelr, to the wider world. There’s at least one comparable package for R, called plm, which is very good and should be particularly appealing for economists. This leads to the understandable question as to how panelr differs from plm.