Implements the algorithms for solving sparse generalized eigenvalue problem by Tan, et. al. (2018). Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow.  To appear in Journal of the Royal Statistical Society: Series B. <arXiv:1604.08697>.

Provides functions to compute recentered influence functions
    (RIF) of a distributional variable at the mean, quantiles, variance,
    gini or any custom functional of interest. The package allows to
    regress the RIF on any number of covariates. Generic print, plot and
    summary functions are also provided. Reference: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2009) <doi:10.3982/ECTA6822>. "Unconditional Quantile Regressions.".

Pointwise generation and display of attractors (prefractals) of the random iterated function system (RIFS) for various combinations of probabilistic and geometric parameters of some fixed point sets (protofractals), described by Bukhovets A.G. (2012) <doi:10.1134/S0005117912020154>.

The goal of Rigma is to provide a user friendly client to the 'Figma'
 API <https://www.figma.com/developers/api>. It uses the latest `httr2` for a 
 stable interface with the REST API. More than 20 methods are provided to 
 interact with 'Figma' files, and teams. Get design data into R by reading 
 published components and styles, converting and downloading images, getting 
 access to the full 'Figma' file as a hierarchical data structure, and much 
 more. Enhance your creativity and streamline the application development by
 automating the extraction, transformation, and loading of design data to your 
 applications and 'HTML' documents.

A set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created 'rigr' to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. 'rigr' output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models.