Package: phenoCDM 0.1.3

Bijan Seyednasrollah

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.

Authors:Bijan Seyednasrollah, Jennifer J. Swenson, Jean-Christophe Domec, James S. Clark

phenoCDM_0.1.3.tar.gz
phenoCDM_0.1.3.tar.gz(r-4.5-noble)phenoCDM_0.1.3.tar.gz(r-4.4-noble)
phenoCDM_0.1.3.tgz(r-4.4-emscripten)phenoCDM_0.1.3.tgz(r-4.3-emscripten)
phenoCDM.pdf |phenoCDM.html✨
phenoCDM/json (API)

# Install 'phenoCDM' in R:
install.packages('phenoCDM', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/bnasr/phenocdm/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

jagscpp

2.00 score 1 stars 164 downloads 6 exports 3 dependencies

Last updated 7 years agofrom:bb6ad3eba4. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 26 2025
R-4.5-linuxOKMar 26 2025
R-4.4-linuxOKMar 26 2025

Exports:fitCDMgetGibbsSummaryphenoSimphenoSimPlotplotPOGibbsplotPost

Dependencies:codalatticerjags

Getting started with phenoCDM

Bijan Seyednasrollah

Rendered fromgetting-started.Rmdusingknitr::rmarkdownon Mar 26 2025.

Last update: 2018-05-02
Started: 2018-05-02

Citation

To cite phenoCDM in publications, please cite both the method and science papers and also the R package in Zenodo:

Bijan Seyednasrollah, Jennifer J. Swenson, Jean-Christophe Domec, James S. Clark, Leaf phenology paradox: Why warming matters most where it is already warm, Remote Sensing of Environment, Volume 209, May 2018, Pages 446-455, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2018.02.059.

Bijan Seyednasrollah, Jennifer J. Swenson, Jean-Christophe Domec, James S. Clark, Continuous development model for incremental time-series analysis: applications on leaf phenology, MethodsX, 2018.

Bijan Seyednasrollah, Jennifer J. Swenson, Jean-Christophe Domec, James S. Clark, (2018). phenoCDM: Continuous Development Models for Incremental Time-Series Analysis<doi:10.5281/zenodo.1204709>

Corresponding BibTeX entries: 

  @Article{,
    author = {Bijan Seyednasrollah and Jennifer J. Swenson and
      Jean-Christophe Domec and James S. Clark},
    title = {Leaf phenology paradox: Why warming matters most where it
      is already warm},
    journal = {Remote Sensing of Environment},
    volume = {209},
    pages = {446 - 455},
    year = {2018},
    issn = {0034-4257},
    doi = {10.1016/j.rse.2018.02.059},
    url =
      {https://www.sciencedirect.com/science/article/pii/S0034425718300713},
  }

  @Article{,
    author = {Bijan Seyednasrollah and Jennifer J. Swenson and
      Jean-Christophe Domec and James S. Clark},
    title = {Continuous development model for incremental time-series
      analysis: applications on leaf phenology},
    publisher = {Elsevier},
    journal = {MethodsX},
    year = {2018},
  }

  @Manual{,
    author = {Bijan Seyednasrollah and Jennifer J. Swenson and
      Jean-Christophe Domec and James S. Clark},
    title = {phenoCDM: Continuous Development Models for Incremental
      Time-Series Analysis},
    year = {2018},
    url = {http://doi.org/10.5281/zenodo.1204709},
  }