Deliverables

At UFDS we strive for a variety of types of deliverables that benefit the FIA Program and our students: from software to peer-reviewed publications to dashboards and more. On this page, we showcase some of our deliverables.

Publications

White, G. W., Yamamoto, J. K., Elsyad, D. H., Schmitt, J. F., Korsgaard, N. H., Hu, J. K., Gaines III, G. C., Frescino, T. S., & McConville, K. S. (2025). Small area estimation of forest biomass via a two-stage model for continuous zero-inflated data. Canadian Journal of Forest Research, 55, 1–19.
Wojcik, O. C., Olson, S. D., Nguyen, P.-H. V., McConville, K. S., Moisen, G. G., & Frescino, T. S. (2022). GREGORY: A modified generalized regression estimator approach to estimating forest attributes in the interior western US. Frontiers in Forests and Global Change, 4, 763414.
White, G. W., McConville, K. S., Moisen, G. G., & Frescino, T. S. (2021). Hierarchical bayesian small area estimation using weakly informative priors in ecologically homogeneous areas of the interior western forests. Frontiers in Forests and Global Change, 4, 752911.
Basil, M. R., Huque, S., McConville, K. S., Moisen, G., & Frescino, T. (2020). Creating homogenous landfire vegetation classes for forest inventory applications in the interior west. In: Brandeis, Thomas j., Comp. Celebrating Progress, Possibilities, and Partnerships: Proceedings of the 2019 Forest Inventory and Analysis (FIA) Science Stakeholder Meeting; November 19-21, 2019; Knoxville, TN. E-Gen. Tech. Rep. SRS-256. Asheville, NC: US Department of Agriculture Forest Service, Southern Research Station. P. 254-267., 254–267.
Rintoul, M. A., Maebius, S., Alvarado, E., Lloyd-Damnjanovic, A., Toyohara, M., McConville, K. S., Moisen, G., & Frescino, T. (2020). An alternative post-stratification scheme to decrease variance of forest attribute estimates in the interior west. In: Brandeis, Thomas j., Comp. Celebrating Progress, Possibilities, and Partnerships: Proceedings of the 2019 Forest Inventory and Analysis (FIA) Science Stakeholder Meeting; November 19-21, 2019; Knoxville, TN. E-Gen. Tech. Rep. SRS-256. Asheville, NC: US Department of Agriculture Forest Service, Southern Research Station. P. 268-276., 256, 268–276.

Software

Russell, L., Wu, W., White, G., McConville, K., & Gaines, G. (2026). Basal: Bayesian small area estimation library. https://github.com/ufds-lab/basal
Yamamoto, J., Elsyad, D., White, G., Schmitt, J., Korsgaard, N., McConville, K., & Hu, K. (2024). Saeczi: Small area estimation for continuous zero inflated data. https://doi.org/10.32614/CRAN.package.saeczi
McConville, K., Yamamoto, J., Tang, B., Zhu, G., White, G., Cheung, S., Li, S., & Toth, D. (2018). Mase: Model-assisted survey estimation. https://cran.r-project.org/package=mase

Student Awards