Retail shopping transactions for 2,469 households over one year.
    Originates from the 84.51° Complete Journey 2.0 source files 
    <https://www.8451.com/area51> which also includes useful metadata on 
    products, coupons, campaigns, and promotions.

Allows for the easy computation of complexity: the proportion of the parameter space in line with the hypothesis by chance. The package comes with a Shiny application in which the calculations can be conducted as well.

Tools for linear fitting with complex variables. Includes ordinary least-squares (zlm()) and robust M-estimation (rzlm()), and complex methods for oft used generics. Originally adapted from the rlm() functions of 'MASS' and the lm() functions of 'stats'.

Providing a set of functions to easily generate and iterate complex networks.
 The functions can be used to generate realistic networks with a wide range of different clustering, density, and average path length.
 For more information consult research articles by Amiyaal Ilany and Erol Akcay (2016) <doi:10.1093/icb/icw068> and Ilany and Erol Akcay (2016) <doi:10.1101/026120>, which have inspired many methods in this package.

Extension of several functions to the complex domain, including the matrix exponential and logarithm, and the determinant.

UpSet plots are an improvement over Venn Diagram for set overlap visualizations.
    Striving to bring the best of the 'UpSetR' and 'ggplot2', this package offers a way to create
    complex overlap visualisations, using simple and familiar tools, i.e. geoms of 'ggplot2'.
    For introduction to UpSet concept, see Lex et al. (2014) <doi:10.1109/TVCG.2014.2346248>.

Robust regression methods for compositional data.
    The distribution of the estimates can be approximated with various bootstrap
    methods. These bootstrap methods are available for the compositional as well
    as for standard robust regression estimates. This allows for direct
    comparison between them.

A suite of computer model test functions that can be used to test and evaluate algorithms for Bayesian (also known as sequential) optimization. Some of the functions have known functional forms, however, most are intended to serve as black-box functions where evaluation requires running computer code that reveals little about the functional forms of the objective and/or constraints. The primary goal of the package is to provide users (especially those who do not have access to real computer models) a source of reproducible and shareable examples that can be used for benchmarking algorithms. The package is a living repository, and so more functions will be added over time. For function suggestions, please do contact the author of the package.