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.
| Version: | 0.1.2 |
| Depends: | R (≥ 3.0.0) |
| Imports: | rjags |
| Published: | 2018-03-20 |
| Author: | Bijan Seyednasrollah, Jennifer Swenson, Jean-Christophe Domec, James S. Clark |
| Maintainer: | Bijan Seyednasrollah <bijan.s.nasr at gmail.com> |
| BugReports: | https://github.com/bnasr/phenoCDM/issues |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Citation: | phenoCDM citation info |
| CRAN checks: | phenoCDM results |
| Reference manual: | phenoCDM.pdf |
| Package source: | phenoCDM_0.1.2.tar.gz |
| Windows binaries: | r-devel: phenoCDM_0.1.2.zip, r-release: phenoCDM_0.1.2.zip, r-oldrel: phenoCDM_0.1.2.zip |
| OS X binaries: | r-release: phenoCDM_0.1.2.tgz, r-oldrel: phenoCDM_0.1.2.tgz |
| Old sources: | phenoCDM archive |
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