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).

The separate p-values of SNPs, RNA expressions and DNA
    methylations are calculated by KM regression. The correlation between
    different omics data are taken into account. This method can be applied to
    either samples with all three types of omics data or samples with two types.

Creates mock data for testing and package development for the
    Observational Medical Outcomes Partnership common data model. The
    package offers functions crafted with pipeline-friendly
    implementation, enabling users to effortlessly include only the
    necessary tables for their testing needs.