Welcome to Models for Missing Data #
This website serves as a storehouse of information about software used by several National Park Service Inventory & Monitoring (I&M) networks to develop models of the status and trends of park resources. It also features general-interest articles introducing statistical concepts and applications of models.
Sections #
Getting started #
Installation instructions, a rundown of the requirements of the software, and a quickstart tutorial for those looking to be hands-on immediately with demo data.
Guide #
Technical information about the software, including inputs and outputs. We describe the data, the metadata syntax used to specify models, and the various outputs of the program, from model checking to inference.
Best Practices #
Modeling best practices, including exploratory data analysis and troubleshooting.
Contributors #
The contributing team includes Luke Zachmann, Tom Hobbs, Erin Borgman, Dana Witwicki, Megan Swan, Cheryl McIntyre, and Carolyn Livensperger, with oversight and support by Dusty Perkins.
Recommended citation #
The work described in these pages is based on Zachmann et al. ( Citation: 2022 Zachmann, L., Borgman, E., Witwicki, D., Swan, M., McIntyre, C. & Hobbs, N. (2022). Bayesian models for analysis of inventory and monitoring data with non-ignorable missingness. Journal of Agricultural, Biological and Environmental Statistics, 27(1). 125–148. ) :
- Zachmann, Borgman, Witwicki, Swan, McIntyre & Hobbs (2022)
- Zachmann, L., Borgman, E., Witwicki, D., Swan, M., McIntyre, C. & Hobbs, N. (2022). Bayesian models for analysis of inventory and monitoring data with non-ignorable missingness. Journal of Agricultural, Biological and Environmental Statistics, 27(1). 125–148.