Peer-reviewed papers
Ryan Martin, Shih-Ni Prim, and Jonathan P. Williams. "Decision-making with possibilistic inferential models,” arXiv: 2112.13247. (In review at the International Journal of Approximate Reasoning.)
Link: Preprint in arXiv.
Shih-Ni Prim, Yawen Guan, Shu Yang, Ana G Rappold, K. Lloyd Hill, Wei-Lun Tsai, Corinna Keeler, and Brian J Reich. "A Spectral Confounder Adjustment for Spatial Regression with Multiple Exposures and Outcomes," arXiv: 2506:09325. (Under revision at the Journal of the American Statistical Association)
Link: Preprint in arXiv.
Code: Github repository.
Helis CA, Prim S, Cramer CK, Strowd R, Lesser GJ, White JJ, Tatter SB, Laxton AW, Whitlow C, Lo H, Debinski W, Ververs JD, Black PJ, Chan MD, “Clinical outcomes of dose-escalated re-irradiation in patients with recurrent high-grade glioma.” Neuro-Oncology Practice (2022).
Link: Paper
Working Papers
Shih-Ni Prim, Kevin R. Quinlan, Paul Hawkins, Jagadeesh Movva, and Annie Booth. "Actively Learning Joint Contours of Multiple Computer Experiments," arXiv: 2512:13530. (Manuscript completed and will be submitted to Technometrics)
Link: Preprint in arXiv.
Code: Bitbucket repository.
Edward Phlips, Shih-Ni Prim, and Natalie Nelson. "Using machine learning to understand harmful algal bloom events."
Online Resources
Natalie G. Nelson, Shih-Ni Prim, Sheila Saia, Khara Grieger, Anders Huseth (authors vary by chapter). 2025. A Machine Learning Primer for Natural Resources Management, Accessed online via go.ncsu.edu/mlprimer.
Link: ML Primer.
Textbook chapters
Chan MD, Prim S, Cramer C, Ruiz J. “Nonrandomized trials in clinical oncology.” In Handbook for Designing and Conducting Clinical and Translational Research: Translational Radiation Oncology, edited by Adam E. M. Eltorai, Jeffrey A. Bakal, Daniel W. Kim, David E. Wazer, 313–284. 2023. Academic Press.
Link: Online reference