Provides a comprehensive suite of tools for analyzing omics data. It includes
           functionalities for alpha diversity analysis, beta diversity analysis, differential
           abundance analysis, community assembly analysis, visualization of phylogenetic tree, and
           functional enrichment analysis. With a progressive approach, the package offers a range of
           analysis methods to explore and understand the complex communities. It is designed to
           support researchers and practitioners in conducting in-depth and professional omics data
           analysis.

Classes and methods for modelling and simulation of
    periodically correlated (PC) and periodically integrated time
    series.  Compute theoretical periodic autocovariances and related
    properties of PC autoregressive moving average models. Some original
    methods including Boshnakov & Iqelan (2009)
    <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996)
    <doi:10.1111/j.1467-9892.1996.tb00281.x>.

Offers a range of utilities and functions for everyday programming tasks. 1.Data
           Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File
           Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities
           for generating color palettes, validating color formats, and adding transparency.
           4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing
           corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides
           efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams,
           including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for
           creating stacked bar plots, or a box plot with alphabets group for multiple comparison group.

Implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) <doi:10.1016/j.aca.2023.341096>.