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phenoCDM
View on CRAN: Click
here
Download and install phenoCDM package within the R console
Install from CRAN:
install.packages("phenoCDM")
Install from Github:
library("remotes")
install_github("cran/phenoCDM") Install by package version:
library("remotes")
install_version("phenoCDM", "0.1.3") Attach the package and use:
library("phenoCDM")
Maintained by
Bijan Seyednasrollah
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-02-16
Latest Update: 2018-05-02
Description:
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.
How to cite:
Bijan Seyednasrollah (2018). phenoCDM: Continuous Development Models for Incremental Time-Series Analysis. R package version 0.1.3, https://cran.r-project.org/web/packages/phenoCDM. Accessed 18 Sep. 2026.
Previous versions and publish date:
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Other R packages that phenoCDM depends,
imports, suggests or enhances
Complete documentation for phenoCDM
Functions, R codes and Examples using
the phenoCDM R package
Some associated functions: fitCDM . getGibbsSummary . phenoSim . phenoSimPlot . plotPOGibbs . plotPost .
Some associated R codes: fitCDM.R . getGibbsSumamry.R . phenoSim.R . phenoSimPlot.R . plotPOGibbs.R . plotPost.R . Full phenoCDM package functions and examples
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