cardinalR: Generating Interesting High-Dimensional Data Structures

TL;DR

cardinalR is an R package that provides new methods for generating diverse high-dimensional data structures, aiding researchers in testing and improving analytical algorithms, especially in nonlinear dimension reduction.

Contribution

The paper introduces novel data generation methods and an R package for creating complex high-dimensional datasets with various structures.

Findings

Provides a versatile R package for high-dimensional data simulation

Enables testing of dimension reduction and learning algorithms

Includes example datasets for benchmarking and validation

Abstract

Simulated high-dimensional data is useful for testing, validating, and improving algorithms used in dimension reduction, supervised and unsupervised learning. High-dimensional data is characterized by multiple variables that are dependent or associated in some way, such as linear, nonlinear, clustering or anomalies. Here we provide new methods for generating a variety of high-dimensional structures using mathematical functions and statistical distributions organized into the R package cardinalR. Several example data sets are also provided. These will be useful for researchers to better understand how different analytical methods work and can be improved, with a special focus on nonlinear dimension reduction methods. This package enriches the existing toolset of benchmark datasets for evaluating algorithms.