Tools for analyzing remote sensing forest data, including functions for detecting treetops from canopy models, outlining tree crowns, and calculating textural metrics.

Project Customer Retention based on Beta Geometric, Beta Discrete Weibull and Latent Class 
    Discrete Weibull Models.This package is based on Fader and Hardie (2007) 
    <doi:10.1002/dir.20074> and Fader and Hardie et al. (2018) <doi:10.1016/j.intmar.2018.01.002>.

Helper functions with a consistent interface to coerce and verify
    the types and shapes of values for input checking.

Reads cell contents plus formatting from a spreadsheet file and creates an editable 'gt' object with the same data and formatting. Supports the most commonly-used cell and text styles including colors, fills, font weights and decorations, and borders.

Tabacchi et al. (2011) published a very detailed study producing a uniform system of functions to estimate tree volume and
    phytomass components (stem, branches, stool). The estimates of the 2005 Italian forest inventory (<https://www.inventarioforestale.org/it/>) 
    are based on these functions. The study documents the domain of applicability of each function and the equations to quantify estimates
    accuracies for individual estimates as well as for aggregated estimates. This package makes the functions available in the R environment. 
    Version 2 exposes two distinct functions for individual and summary estimates. To facilitate access to the functions, tree species 
    identification is now based on EPPO species codes (<https://data.eppo.int/>).

Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the 'ForLion' algorithm and 'EW ForLion' algorithm. The algorithms searches for locally optimal designs and EW optimal designs under the D-criterion. See Huang, Y., Li, K., Mandal, A., & Yang, J., (2024) <doi:10.1007/s11222-024-10465-x> and Lin, S., Huang, Y., & Yang, J. (2025) <doi:10.48550/arXiv.2505.00629>.