Functions for visualizing, animating, solving and 
        analyzing the Rubik's cube. Includes data structures for
		solvable and unsolvable cubes, random moves and random 
		state scrambles and cubes, 3D displays and animations 
		using 'OpenGL', patterned cube generation, and lightweight 
		solvers. See Rokicki, T. (2008) <arXiv:0803.3435> for the
		Kociemba solver.

Regression modeling using rules with added instance-based
    corrections.

Write executable specifications in a natural language that describes how your code should behave.
    Write specifications in feature files using 'Gherkin' language and execute them using functions implemented in R.
    Use them as an extension to your 'testthat' tests to provide a high level description of how your code works.

R interface for RAPIDS cuML (<https://github.com/rapidsai/cuml>),
    a suite of GPU-accelerated machine learning libraries powered by CUDA
    (<https://en.wikipedia.org/wiki/CUDA>).

Utility functions that provides wrapper to descriptive base functions
  like cor, mean and table.  It makes use of the formula interface to pass
  variables to functions.  It also provides operators to concatenate (%+%), to
  repeat (%n%) and manage character vectors for nice display.

Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy.

Provides similar functionality to 'Microsoft Excel' 'CUMPRINC' function <https://support.microsoft.com/en-us/office/cumprinc-function-94a4516d-bd65-41a1-bc16-053a6af4c04d>. 
    Returns principal remaining at a given month, principal paid in a month, and accumulated principal paid at a given month based on original loan amount, 
    monthly interest rate, and term of loan.

Tools for visualization of, and inference on, the calibration of prediction models on the cumulative domain. This provides a method for evaluating calibration of risk prediction models without having to group the data or use tuning parameters (e.g., loess bandwidth). This package implements the methodology described in Sadatsafavi and Petkau (2024) <doi:10.1002/sim.10138>. The core of the package is cumulcalib(), which takes in vectors of binary responses and predicted risks. The package also implements non-parametric assessment of the calibration of individualized treatment effect (ITE) models using data from a randomized trial, via cumulcalibITE(), as described in Sadatsafavi et al. (2025) <doi:10.48550/arXiv.2512.08140>. The plot() and summary() methods are implemented for the results returned by cumulcalib() and cumulcalibITE().