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>.

Provides tools for processing, analyzing, and visualizing spectral data 
    collected from 3D laser-based scanning systems. Supports applications in agriculture, 
    forestry, environmental monitoring, industrial quality control, and biomedical research. 
    Enables evaluation of plant growth, productivity, resource efficiency, disease 
    management, and pest monitoring. Includes statistical methods for extracting insights 
    from multispectral and hyperspectral data and generating publication-ready visualizations. 
    See Zieschank & Junker (2023) <doi:10.3389/fpls.2023.1141554> and 
    Saric et al. (2022) <doi:10.1016/J.TPLANTS.2021.12.003> for related work.