Performing multivariate imputation by chained equations

Multivariate imputation methods, as opposed to univariate imputation, use the entire set of variables to estimate the missing values. In other words, the missing values of a variable are modeled based on the other variables in the dataset. Multivariate imputation by chained equations (MICE) is a multiple imputation technique that models each variable with missing values as a function of the remaining variables and uses that estimate for imputation. MICE has the following basic steps:

1. A simple univariate imputation is performed for every variable with missing data, for example, median imputation.
2. One specific variable is selected, say, var_1, and the missing values are set back to missing.
3. A model that's used to predict var_1 is built based on the remaining variables in the dataset.
4. The missing values...