Provides functions that allow users to quantify the relative 
	contributions of geographic and ecological distances to empirical patterns of genetic 
	differentiation on a landscape.  Specifically, we use a custom Markov chain 
	Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the 
	inference model, as well as functions for performing MCMC diagnosis and assessing 
	model adequacy.

A matrix-like data structure that allows for efficient,
    convenient, and scalable subsetting of binary genotype/phenotype files
    generated by PLINK (<https://www.cog-genomics.org/plink2>), the whole
    genome association analysis toolset, without loading the entire file into
    memory.

Genomic regions processing using open-source command line tools such as 'BEDTools', 'BEDOPS' and 'Tabix'.
  These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging.
  bedr API enhances access to these tools as well as offers additional utilities for genomic regions processing.

Efficiently access the 'Bedrock Bio' library of open-access
    computational biology datasets. Lazily query datasets backed by 'DuckDB'
    and 'Apache Iceberg', with support for predicate pushdown and column
    projection to the cloud storage backend. This enables quick, iterative
    access to otherwise massive, unwieldy datasets without downloading them
    in full. See <https://bedrock.bio> for available datasets and
    documentation.

Flags and checks occurrence data that are in Darwin Core
    format.  The package includes generic functions and data as well as
    some that are specific to bees. This package is meant to build upon
    and be complimentary to other excellent occurrence cleaning packages,
    including 'bdc' and 'CoordinateCleaner'.  This package uses datasets
    from several sources and particularly from the Discover Life Website,
    created by Ascher and Pickering (2020).  For further information,
    please see the original publication and package website.  Publication
    - Dorey et al. (2023) <doi:10.1101/2023.06.30.547152> and package
    website - Dorey et al. (2023) <https://github.com/jbdorey/BeeBDC>.