Functions that allow you to create your own color palette from an image, using mathematical algorithms.

A suite of tools for transforming an existing workflow into a
    self-documenting pipeline with very minimal upfront costs. Segments of
    the pipeline are specified in much the same way a 'Make' rule is, by
    declaring an executable recipe (which might be an R script), along
    with the corresponding targets and dependencies. When the entire
    pipeline is run through, only those recipes that need to be executed
    will be. Meanwhile, execution metadata is captured behind the scenes
    for later inspection.

This package creates an empty framework of files and
        directories for the "Load, Clean, Func, Do" structure described
        by Josh Reich.

Helper functions that interface with the system utilities to learn 
    about the local build environment. Lets you explore 'make' rules to test the
    local configuration, or query 'pkg-config' to find compiler flags and libs 
    needed for building packages with external dependencies. Also contains tools
    to analyze which libraries that a installed R package linked to by inspecting
    output from 'ldd' in combination with information from your distribution 
    package manager, e.g. 'rpm' or 'dpkg'.

Make all elements of a character vector unique. 
    Differs from 'make.unique' by starting at 1 and allowing users to customise suffix format.

R package associated with the Multiple Approximate Kernel Learning (MAKL) algorithm proposed in <doi:10.1093/bioinformatics/btac241>. The algorithm fits multiple approximate kernel learning (MAKL) models that are fast, scalable and interpretable.

MAle Lineage ANalysis by simulating 
    genealogies backwards and imposing short tandem repeats (STR) mutations forwards. 
    Intended for forensic Y chromosomal STR (Y-STR) haplotype analyses. 
    Numerous analyses are possible, e.g. number of matches and meiotic distance to matches. 
    Refer to papers mentioned in citation("malan") (DOI's: 
    <doi:10.1371/journal.pgen.1007028>, 
    <doi:10.21105/joss.00684> and 
    <doi:10.1016/j.fsigen.2018.10.004>).