  It is critical to accurately simulate data when employing Monte Carlo
techniques and evaluating statistical methodology. Measurements are often
correlated and high dimensional in this era of big data, such as data obtained
in high-throughput biomedical experiments. Due to the computational complexity
and a lack of user-friendly software available to simulate these massive
multivariate constructions, researchers resort to simulation designs that posit
independence or perform arbitrary data transformations. To close this gap, we
developed the Bigsimr Julia package with R and Python interfaces. This paper
focuses on the R interface. These packages empower high-dimensional random
vector simulation with arbitrary marginal distributions and dependency via a
Pearson, Spearman, or Kendall correlation matrix. bigsimr contains
high-performance features, including multi-core and
graphical-processing-unit-accelerated algorithms to estimate correlation and
compute the nearest correlation matrix. Monte Carlo studies quantify the
accuracy and scalability of our approach, up to $d=10,000$. We describe example
workflows and apply to a high-dimensional data set -- RNA-sequencing data
obtained from breast cancer tumor samples.
