In bulk epigenome/transcriptome experiments, molecular expression
    is measured in a tissue, which is a mixture of multiple types of cells.
    This package tests association of a disease/phenotype with a molecular marker
    for each cell type.
    The proportion of cell types in each sample needs to be given as input.
    The package is applicable to epigenome-wide association study (EWAS) and
    differential gene expression analysis.
    Takeuchi and Kato (submitted)
    "omicwas: cell-type-specific epigenome-wide and transcriptome association study".

Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) <doi:10.1037/1082-989X.12.4.414>.

Provides a suite of tools for the comprehensive visualization of multi-omics data, including genomics, transcriptomics, and proteomics. Offers user-friendly functions to generate publication-quality plots, thereby facilitating the exploration and interpretation of complex biological datasets. Supports seamless integration with popular R visualization frameworks and is well-suited for both exploratory data analysis and the presentation of final results. Key formats and methods are presented in Huang, S., et al. (2024) "The Born in Guangzhou Cohort Study enables generational genetic discoveries" <doi:10.1038/s41586-023-06988-4>.

An assortment of helper functions for managing data (e.g.,
	rotating values in matrices by a user-defined angle, switching from
	row- to column-indexing), dates (e.g., intuiting year from messy date
	strings), handling missing values (e.g., removing elements/rows across
	multiple vectors or matrices if any have an NA), text (e.g.,
	flushing reports to the console in real-time); and combining data frames
	with different schema (copying, filling, or concatenating columns or
	applying functions before combining).