Running summarizing tools on a new article

Abstract: This chapter explores how to generate synthetic data using the R programming language. We start by looking at some basic functions used to generate synthetic data. Next, we look at how to construct a value vector from a known univariate distribution and how to construct a vector from a multi-level categorical variable. We look at how to build a neural network in R using the nnet package. We give an example of image augmentation using a torch package. We also explore generating synthetic data in R using the mice conjurer and synthpop package. Finally, copulas and R practices related to the t copula, normal copula, and Gaussian copula are covered.