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.

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)'.

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.

Performs likelihood-based inference for stationary time series 
    extremes.  The general approach follows Fawcett and Walshaw (2012)
    <doi:10.1002/env.2133>.  Marginal extreme value inferences are adjusted for 
    cluster dependence in the data using the methodology in Chandler and Bate 
    (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood 
    for the model parameters.  A log-likelihood for the extremal index is 
    produced using the K-gaps model of Suveges and Davison (2010) 
    <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make 
    inferences about extreme values. Both maximum likelihood and Bayesian 
    approaches are available.