Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development.
Package details |
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Author | Bijan Seyednasrollah, Jennifer J. Swenson, Jean-Christophe Domec, James S. Clark |
Maintainer | Bijan Seyednasrollah <bijan.s.nasr@gmail.com> |
License | AGPL-3 | file LICENSE |
Version | 0.1.4 |
Package repository | View on GitHub |
Installation |
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