The aim of the package is to create data objects which allow for accesses like x["test"] and x["test","test"].

A toolbox for R arrays. Flexibly split, bind, reshape, modify, 
    subset and name arrays.

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.

Programmatically create R Markdown documents from lists.

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)'.

Tests for independence between X and Y computed from a paired sample (x1,y1),...(xn,yn) of (X,Y), using one of the following statistics (a) the Longest Increasing Subsequence (Ln), (b) JLn, a Jackknife version of Ln or (c) JLMn, a Jackknife version of the longest monotonic subsequence. This family of tests can be applied under the assumption of continuity of X and Y.

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.