Benchmarking different imputation methods

This documentation describes how the MicroImpute package allows you to compare different imputation methods using quantile loss metrics.

The benchmarking functionality enables systematically comparing multiple imputation models using a common dataset, allowing for robust evaluation of their performance. By assessing accuracy across various quantiles, you gain a comprehensive understanding of how each method performs across different levels of the distribution. This process is further supported by visualizations that highlight differences between approaches, making it easy to identify which imputation methods perform best under specific conditions. Ultimately, this empowers you to make data-driven decisions regarding the most suitable imputation approach for your analysis or application.