Easy installation, loading and management, of high-performance packages 
             for statistical computing and data manipulation in R. 
             The core 'fastverse' consists of 4 packages: 'data.table', 'collapse', 
             'kit' and 'magrittr', that jointly only depend on 'Rcpp'. 
             The 'fastverse' can be freely and permanently extended with 
             additional packages, both globally or for individual projects. 
             Separate package verses can also be created. Fast packages 
             for many common tasks such as time series, dates and times, strings, 
             spatial data, statistics, data serialization, larger-than-memory 
             processing, and compilation of R code are listed in the README file: 
             <https://github.com/fastverse/fastverse#suggested-extensions>.

A lightweight package to compute Maximal Overlap Discrete Wavelet 
    Transform (MODWT) and À Trous Discrete Wavelet Transform by leveraging the 
    power of 'Rcpp' to make these operations fast. This package was designed for use in forecasting, and
    allows users avoid the inclusion of future data when performing wavelet decomposition of time series.
    See Quilty and Adamowski (2018) <doi:10.1016/j.jhydrol.2018.05.003>.

Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models
             defined by some design matrix.

Application of 'FateID' allows computation and visualization of cell fate bias for multi-lineage single cell transcriptome data. Herman, J.S., Sagar, Grün D. (2018) <DOI:10.1038/nmeth.4662>.

Kiener distributions K1, K2, K3, K4 and K7 to characterize
    distributions with left and right, symmetric or asymmetric fat tails in market
    finance, neuroscience and other disciplines. Two algorithms to estimate with
    a high accuracy distribution parameters, quantiles, value-at-risk and expected
    shortfall. Include power hyperbolas and power hyperbolic functions.

Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://debruine.github.io/faux/>. Described in DeBruine (2020) <doi:10.5281/zenodo.2669586>.