One advanced technique for optimizing code performance in R is vectorization, which involves performing operations on entire vectors or matrices instead of using loops. This can significantly speed up calculations. Another approach is to use the `compiler` package, which transforms R code into byte code and can improve execution speed. Additionally, you can utilize packages like `data.table` or `dplyr` for efficient data manipulation, as they are optimized for performance.

Apart from the aforementioned techniques, utilizing the `Rcpp` package can greatly enhance code performance in R. Rcpp allows you to write C++ code and seamlessly integrate it with R, leveraging the speed and efficiency of C++. This can be particularly beneficial for computationally intensive tasks. Another approach is to profile your code using tools like `profvis` or `microbenchmark`. Profiling helps identify bottlenecks and inefficient parts of your code, allowing you to optimize those sections for better performance.

In addition to vectorization and using the `compiler` package, you can also consider parallel processing in R to improve performance. The `parallel` package enables you to distribute computations across multiple cores or even across multiple machines. By leveraging multiple processors, you can significantly speed up your code execution. Another technique is to use memoization, which involves caching function results to avoid redundant computations. The `memoise` package provides a simple way to implement this technique in R.