Kernel Regularized Least Squares (KRLS) is a kernel-based, complexity-penalized method developed by Hainmueller and Hazlett (2013), and designed to minimize parametric assumptions while maintaining interpretive clarity. Here, we introduce bigKRLS, an updated version of the original KRLS R package with algorithmic and implementation improvements designed to optimize speed and memory usage. These improvements allow users to straightforwardly estimate pairwise regression models with KRLS once N > 2500. Since April 15, 2017, bigKRLS has been available on CRAN. You may also be interested in our working paper, which has been accepted by Political Analysis, and which demonstrates the utility of bigKRLS by analyzing the 2016 US presidential election. Our replication materials can be found on Dataverse and our Github repo contains examples too. C++ integration. We re-implement most major computations in the model in C++ via Rcpp and RcppArmadillo. These changes produce up to a 50% runtime decrease compared to the original R implementation.