Years ago I had written a post on using multiple imputation, weights, and accounting for clustering using R. However, the process was actually quite cumbersome and now in 2024, there are more straightforward ways of handling this.
1. Load in the required packges library(dplyr) #for basic data management library(tidyr) #converting wide to tall library(estimatr) #estimating models with robust SEs library(mitml) #for imputation and analyzing MI datasets library(MLMusingR) #contains the sample dataset library(mice) #for carrying out the analysis with MI data library(modelsummary) #outputting the results nicely library(survey) #alternative (classic) way 2.