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.

The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and 
    growing season dividing method and a practical snow elimination method 
    based on Whittaker were proposed. 7 curve fitting methods and 4 phenology 
    extraction methods were provided. Parameters boundary are considered for 
    every curve fitting methods according to their ecological meaning. 
    And 'optimx' is used to select best optimization method for different 
    curve fitting methods.
    Reference:
    Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction 
    package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>;
    Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains 
    the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness 
    Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. 
    <doi:10.1029/2020JG005636>;
    Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing 
    global MODIS EVI time series on the Google Earth Engine. 
    ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24;
    Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology 
    on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). 
    Agric. For. Meteorol. 248, 408–417. <doi:10.1016/j.agrformet.2017.10.026>.

Fit a time-series model 
    to a crop phenology data, such as time-series rice canopy height.
    This package returns the model parameters as the summary statistics of crop phenology,
    and these parameters will be useful to characterize the growth pattern of each cultivar and 
    predict manually-measured traits, such as days to heading and biomass.
    Please see Taniguchi et al. (2022) <doi:10.3389/fpls.2022.998803> and 
    Taniguchi et al. (2025) <doi: 10.3389/frai.2024.1477637> for detail.
    This package has been designed for scientific use.  
    Use for commercial purposes shall not be allowed.