An implementation of list comprehensions as purely syntactic
  sugar with a minor runtime overhead. It constructs nested for-loops and
  executes the byte-compiled loops to collect the results.

Syntactic shortcuts for creating synthetic lists, vectors, 
    data frames, and matrices using list comprehension.

List environments are environments that have list-like properties.  For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a = 1, b = 2); for (i in seq_along(x)) x[[i]] <- x[[i]] ^ 2; y <- as.list(x)'.

The implementation of a statistical framework for performing overlap assessments on
    lists comprising sets of strings (such as lists of gene sets) described in Stoica (2023)
    <https://ora.ox.ac.uk/objects/uuid:b0847284-a02f-47ee-88e3-a3c4e0cdb8b1>.
    It can assess overlaps of pairs of sets of strings selected either from the same universe or 
    from different universes, and overlaps of triplets of sets of strings selected from the same
    universe. Designed for single-cell RNA-sequencing data analysis applications, but suitable
    for other purposes as well.

Tools for common operations on lists. Provided are short-cuts to
    operations like selecting and merging data stored in lists. The functions in
    this package are designed to be used with pipes.

R lists, especially nested lists, can be very difficult to
    visualize or represent. Sometimes 'str()' is not enough, so this suite of
    htmlwidgets is designed to help see, understand, and maybe even modify your R
    lists.  The function 'reactjson()' requires a package
    'reactR' that can be installed from CRAN or <https://github.com/timelyportfolio/reactR>.

Provides a function that, as an alternative to base::list, allows
    default values to be inherited from another list.

Identifying latent genetic interactions in genome-wide association studies
    using the Latent Interaction Testing (LIT) framework.
    LIT is a flexible kernel-based approach that leverages information across
    multiple traits to detect latent genetic interactions without specifying or
    observing the interacting variable (e.g., environment). LIT accepts
    standard PLINK files as inputs to analyze large genome-wide association studies.