Produces forest plots using 'ggplot2' from models produced by functions
    such as stats::lm(), stats::glm() and survival::coxph().

A forest plot that allows for
    multiple confidence intervals per row,
    custom fonts for each text element,
    custom confidence intervals,
    text mixed with expressions, and more.
    The aim is to extend the use of forest plots beyond meta-analyses.
    This is a more general version of the original 'rmeta' package's forestplot()
    function and relies heavily on the 'grid' package.

Create a forest plot based on the layout of the data. Confidence intervals in multiple columns by groups can be done easily. Editing the plot, inserting/adding text, applying a theme to the plot, and much more.

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.

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