Another option is to utilize the data.table package in R. By converting your vector into a data.table object, you can use its fast aggregation functions like sum() and reduce() to achieve faster results compared to using the base R functions. This can be especially useful when dealing with large datasets.

You can use the sum() function to calculate the sum of all elements in a numeric vector in R. However, for large vectors, this approach may result in slower performance. In such cases, you can utilize the Rcpp package to write a C++ function that performs the summation using low-level optimizations, which can significantly improve speed.

Instead of using the sum() function, you can try using the parallel package in R to distribute the sum calculation across multiple processor cores. By using the parallel::mclapply() function, you'll be able to take advantage of parallel processing and potentially speed up the summation process for large vectors.