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 gmail.com>
BugReports:	https://github.com/bnasr/phenoCDM/issues
License:	MIT + file LICENSE
NeedsCompilation:	no
Citation:	phenoCDM citation info
CRAN checks:	phenoCDM results

Documentation:

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

Downloads:

Package source:	phenoCDM_0.1.3.tar.gz
Windows binaries:	r-devel: phenoCDM_0.1.3.zip, r-release: phenoCDM_0.1.3.zip, r-oldrel: phenoCDM_0.1.3.zip
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

Linking:

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