Interactive forest plot for clinical trial safety analysis
    using 'metalite', 'reactable', 'plotly', and Analysis Data Model (ADaM)
    datasets. Includes functionality for adverse event filtering,
    incidence-based group filtering, hover-over reveals, and search and sort
    operations. The workflow allows for metadata construction, data preparation,
    output formatting, and interactive plot generation.

Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). <doi:10.1002/9781118902028>.

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

Allows the creation of forest plots with advanced features, such as 
    multiple confidence intervals per row, customizable fonts for individual text 
    elements, and flexible confidence interval drawing. It also supports mixing 
    text with mathematical expressions. The package extends the application of 
    forest plots beyond traditional meta-analyses, offering a more general version 
    of the original 'rmeta' package’s forestplot() function. It relies heavily on 
    the 'grid' package for rendering the plots.

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.