One advanced technique for optimizing R code is to use vectorization, which allows operations to be performed on entire vectors or matrices instead of individual elements. This can significantly improve performance by reducing the number of function calls and iterations. Another technique is to leverage parallel processing using packages like `parallel` or `foreach` to execute code simultaneously across multiple cores or machines. The `data.table` package also offers fast and memory-efficient alternatives to base R functions for data manipulation. Additionally, profiling tools like `profvis` can help identify bottlenecks in the code and optimize them for better performance.