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>.

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>.

Implements statistical methods for exploratory subgroup identification
    in clinical trials with survival endpoints. Provides tools for identifying
    patient subgroups with differential treatment effects using machine learning
    approaches including Generalized Random Forests (GRF), LASSO regularization,
    and exhaustive combinatorial search algorithms. Features bootstrap bias
    correction using infinitesimal jackknife methods to address selection bias
    in post-hoc analyses. Designed for clinical researchers conducting exploratory
    subgroup analyses in randomized controlled trials, particularly for
    multi-regional clinical trials (MRCT) requiring regional consistency
    evaluation. Supports both accelerated failure time (AFT) and Cox proportional
    hazards models with comprehensive diagnostic and visualization tools.
    Methods are described in León et al. (2024) <doi:10.1002/sim.10163>.