  Computational statistics has traditionally utilized double-precision (64-bit)
data structures and full-precision operations, resulting in
higher-than-necessary accuracy for certain applications. Recently, there has
been a growing interest in exploring low-precision options that could reduce
computational complexity while still achieving the required level of accuracy.
This trend has been amplified by new hardware such as NVIDIA's Tensor Cores in
their V100, A100, and H100 GPUs, which are optimized for mixed-precision
computations, Intel CPUs with Deep Learning (DL) boost, Google Tensor
Processing Units (TPUs), Field Programmable Gate Arrays (FPGAs), ARM CPUs, and
others. However, using lower precision may introduce numerical instabilities
and accuracy issues. Nevertheless, some applications have shown robustness to
low-precision computations, leading to new multi- and mixed-precision
algorithms that balance accuracy and computational cost. To address this need,
we introduce MPCR, a novel R package that supports three different precision
types (16-, 32-, and 64-bit) and their combinations, along with its usage in
commonly-used Frequentist/Bayesian statistical examples. The MPCR package is
written in C++ and integrated into R through the \pkg{Rcpp} package, enabling
highly optimized operations in various precisions.
