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 = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

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

jagscpp

2.00 score 1 stars 8 scripts 160 downloads 6 exports 3 dependencies

Last updated 7 years agofrom:bb6ad3eba4. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 04 2024
R-4.5-linuxOKDec 04 2024

Exports:fitCDMgetGibbsSummaryphenoSimphenoSimPlotplotPOGibbsplotPost

Dependencies:codalatticerjags

Getting started with phenoCDM

Rendered fromgetting-started.Rmdusingknitr::rmarkdownon Dec 04 2024.

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