  The least squares of depth trimmed (LST) residuals regression, proposed in
Zuo and Zuo (2023) \cite{ZZ23}, serves as a robust alternative to the classic
least squares (LS) regression as well as a strong competitor to the famous
least trimmed squares (LTS) regression of Rousseeuw (1984) \cite{R84}.
Theoretical properties of the LST were thoroughly studied in \cite{ZZ23}.
  The article aims to promote the implementation and computation of the LST
residuals regression for a broad group of statisticians in statistical practice
and demonstrates that (i) the LST is as robust as the benchmark of robust
regression, the LTS regression, and much more efficient than the latter. (ii)
It can be as efficient as (or even more efficient than) the LS in the scenario
with errors uncorrelated with mean zero and homoscedastic with finite variance.
(iii) It can be computed as fast as (or even faster than) the LTS based on a
newly proposed algorithm.
