Emily Tupaj (class of 2021)
Emily Tupaj developed R software for a project on constructing confidence intervals from permutation tests. This work was presented at the 2022 useR! Conference. Emily also developed the questions & sampling design and carried out much of the data analysis for a survey about textbook usage at Colby, on behalf of the Library Committee.
• Tupaj, E. and Wieczorek, J. (2022) “CIPerm: Computationally-Efficient Confidence Intervals for Mean Shift from Permutation Methods.” R package version 0.2.3. (CRAN, GitHub)
Cole Guerin (class of 2021)
Thomas McMahon (class of 2021)
Cole Guerin and Thomas McMahon developed R software, simulations, and real-data examples for a project on complex-sample-survey cross-validation. This work was presented at the 2021 Symposium on Data Science and Statistics (SDSS) and published in the journal Stat.
• Guerin, C., McMahon, T., and Wieczorek, J. (2021) “surveyCV: Cross Validation Based on Survey Design.” R package version 0.2.0. (CRAN, GitHub)
• Wieczorek, J., Guerin, C., and McMahon, T. (2022) “K-fold cross-validation for complex sample surveys.” Stat, 11 (1), e454.
Zach Cody (class of 2023)
Emily Tan (class of 2023)
Jackie Chistolini (class of 2024J)
Leonor (Leyang) Rui (class of 2024)
Zach Cody worked on data analysis and R Shiny coding for an interactive data visualization app associated with our paper on “Assessing small area estimates via artificial populations…” Emily Tan and Jackie Chistolini focused on creating interactive maps of the same dataset using Leaflet.js and integrating them into the Shiny app. Leonor Rui and Jackie Chistolini also developed model comparison examples (unpublished work in progress) to illustrate how this data can be used as a test case for small area model selection.
• Cody, Z., Tan, E., Chistolini, J., and Wieczorek, J. “R Shiny dashboard for FIA artificial population.” Companion software to the paper below. (ShinyApps.io, GitHub)
• Wieczorek, J., White, G., Cody, Z., Tan, E., Chistolini, J., McConville, K., Frescino, T., and Moisen, G. “Assessing small area estimates via artificial populations from KBAABB: a kNN-based approximation to ABB.” (arXiv preprint, submitted for review.)
Andy Day (class of 2025)
Andy Day worked on improvements to the R package surveyConformal (unpublished draft in progress), which we intend to release as companion software for the paper “Design-based conformal prediction,” Wieczorek (2023).