Computes relative importance of main and interaction effects. Also, sum of the modified generalized weights is computed. Ibrahim et al. (2022) <doi:10.1134/S1064229322080051>.

Read and write Frictionless Data Packages. A 'Data Package'
    (<https://specs.frictionlessdata.io/data-package/>) is a simple
    container format and standard to describe and package a collection of
    (tabular) data. It is typically used to publish FAIR
    (<https://www.go-fair.org/fair-principles/>) and open datasets.

Converts vectors of numbers into character vectors of numerals,
    including cardinals (one, two, three) and ordinals (first, second, third). 
    Supports negative numbers, fractions, and arbitrary-precision integer and 
    high-precision floating-point vectors provided by the 'bignum' package.

The complete scripts from the American sitcom Friends in tibble 
    format. Use this package to practice data wrangling, text analysis and 
    network analysis.

Miscellaneous utilities, tools and helper
    functions for finding and searching files on disk, searching for and
    removing R objects from the workspace.
    Does not import or depend on any third party package, but on core R 
    only (i.e. it may depend on packages with priority 'base').

A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.