phenoCDM: Continuous Development Models for Incremental Time-Series Analysis

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: 0.1.3
Depends: R (≥ 3.3.0)
Imports: rjags
Suggests: knitr, rmarkdown
Published: 2018-05-02
DOI: 10.32614/CRAN.package.phenoCDM
Author: Bijan Seyednasrollah, Jennifer J. Swenson, Jean-Christophe Domec, James S. Clark
Maintainer: Bijan Seyednasrollah <bijan.s.nasr at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: phenoCDM citation info
CRAN checks: phenoCDM results


Reference manual: phenoCDM.pdf
Vignettes: Getting started with phenoCDM


Package source: phenoCDM_0.1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): phenoCDM_0.1.3.tgz, r-oldrel (arm64): phenoCDM_0.1.3.tgz, r-release (x86_64): phenoCDM_0.1.3.tgz, r-oldrel (x86_64): phenoCDM_0.1.3.tgz
Old sources: phenoCDM archive


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