Introduction to the binsmooth package

Details

Hi all,

For this session, we have the pleasure to have Dave Hunter to present about the binsmooth package (https://github.com/djhunter/binsmooth ; also on CRAN) which grew out of an undergraduate research project Dave supervised a couple years ago. The initial problem was to estimate inequality indexes (e.g., the Gini coefficient) from ACS income data (https://www.census.gov/topics/income-poverty/income/data/datasets.html), which is binned. Their work eventually appeared in Sociological Science (open access link: https://sociologicalscience.com/articles-v4-26-641/). Lessons to R users include the relative ease of contributing to CRAN, and the benefits (and pitfalls) therein.

More about our presenter:
Dave Hunter has been teaching mathematics and computer science at Westmont College since 2000. In the 90's he spent some time teaching high school in the suburbs of Chicago, after which he completed his Ph.D. in mathematics at the University of Virginia, specializing in algebraic topology. His publications include a book, Essentials of Discrete Mathematics, and numerous journal articles such as How rare is symmetry in 12-tone musical rows?, Better Estimates from Binned Income Data: Interpolated CDFs and Mean-Matching, and New metrics for evaluating home plate umpire consistency and accuracy. He has co-directed Westmont in Mexico and enjoys running Santa Barbara’s trails, cheering for the Cubs, and cooking.

PS: You need to RSVP to see the Zoom link