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.

Functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) <doi:10.3354/esr00292> for the phenology function, Girondot, M. (2017) <doi:10.1016/j.ecolind.2017.05.063> for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) <doi:10.2993/etbi-35-02-337-353.1> for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) <doi:10.1007/s00227-019-3545-x> for tag-loss estimate, Hancock J, ..., Girondot M (2019) <doi:10.1016/j.ecolmodel.2019.04.013> for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) <doi:10.1007/s00227-020-03686-x> for aggregating several seasons.

This takes in a series of multi-layer raster files and returns a phenology projection raster, following methodologies described in John (2016) <https://etda.libraries.psu.edu/catalog/13521clj5135>.

A collection of functions to process digital images, depict greenness index trajectories and extract relevant phenological stages.

The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) <doi:10.1093/jamia/ocx111>.