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
Author: Bijan Seyednasrollah, Jennifer J. 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

Downloads:

Reference manual: phenoCDM.pdf
Vignettes: Getting started with phenoCDM
Package source: phenoCDM_0.1.3.tar.gz
Windows binaries: r-devel: phenoCDM_0.1.3.zip, r-devel-gcc8: phenoCDM_0.1.3.zip, r-release: phenoCDM_0.1.3.zip, r-oldrel: phenoCDM_0.1.3.zip
OS X binaries: r-release: phenoCDM_0.1.3.tgz, r-oldrel: phenoCDM_0.1.3.tgz
Old sources: phenoCDM archive

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