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.

Analysis of forest population structure and quantitative dynamics is the research and evaluation of the composition, distribution, age structure and changes in quantity over time of various populations in the forest. By deeply understanding these characteristics of forest populations, scientific basis can be provided for the management, protection and sustainable utilization of forest resources. This R package conducts a systematic analysis of forest population structure and quantitative dynamics through analyzing age structure, compiling life tables, population quantitative dynamic change indices and time series models, in order to provide support for forest population protection and sustainable management. References: Zhang Y, Wang J, Wang X, et al(2024)<doi:10.3390/plants13070946>. Yuan G, Guo Q, Xie N, et al(2023)<doi:10.1007/s11629-022-7429-z>.