Provides a toolkit for calculating forest and canopy structural complexity metrics from
    terrestrial LiDAR (light detection and ranging). References:  Atkins et al. 2018 <doi:10.1111/2041-210X.13061>; Hardiman et al. 2013 <doi:10.3390/f4030537>;
    Parker et al. 2004 <doi:10.1111/j.0021-8901.2004.00925.x>.

Provides functions that calculates common types of splitting
    criteria used in random forests for classification problems, as well as 
    functions that make predictions based on a single tree or a Forest-R.K. model; 
    the package also provides functions to generate importance plot for a 
    Forest-R.K. model, as well as the 2D multidimensional-scaling plot of 
    data points that are colour coded by their predicted class types by the 
    Forest-R.K. model. This package is based on: 
    Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3) 
    "Forest-R.K.: A New Random Forest Induction Method", 
    Fourth International Conference on Intelligent Computing,
    September 2008, Shanghai, China, pp.430-437.

A series of utility functions to help with 
    reshaping hierarchy of data tree, and reform the structure of data tree.

Recent years have seen significant interest in neighborhood-based structural parameters that effectively represent the spatial characteristics of tree populations and forest communities, and possess strong applicability for guiding forestry practices. This package provides valuable information that enhances our understanding and analysis of the fine-scale spatial structure of tree populations and forest stands. Reference: Yan L, Tan W, Chai Z, et al (2019) <doi:10.13323/j.cnki.j.fafu(nat.sci.).2019.03.007>.

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>.