  In Maples et al. (2018) we introduced Robust Chauvenet Outlier Rejection, or
RCR, a novel outlier rejection technique that evolves Chauvenet's Criterion by
sequentially applying different measures of central tendency and empirically
determining the rejective sigma value. RCR is especially powerful for cleaning
heavily-contaminated samples, and unlike other methods such as sigma clipping,
it manages to be both accurate and precise when characterizing the underlying
uncontaminated distributions of data sets, by using decreasingly robust but
increasingly precise statistics in sequence. For this work, we present RCR from
a software standpoint, newly implemented as a Python package while maintaining
the speed of the C++ original. RCR has been well-tested, calibrated and
simulated, and it can be used for both one-dimensional outlier rejection and
$n$-dimensional model-fitting, with or without weighted data. RCR is free to
use for academic and non-commercial purposes, and the code, documentation and
accompanying web calculator can be found and easily used online at
https://github.com/nickk124/RCR
