  The BNL and FNAL measurements of the anomalous magnetic moment of the muon
disagree with the Standard Model (SM) prediction by more than $4\sigma$. The
hadronic vacuum polarization (HVP) contributions are the dominant source of
uncertainty in the SM prediction. There are, however, tensions between
different estimates of the HVP contributions, including data-driven estimates
based on measurements of the $R$-ratio. To investigate that tension, we modeled
the unknown $R$-ratio as a function of CM energy with a treed Gaussian process
(TGP). This is a principled and general method grounded in data-science that
allows complete uncertainty quantification and automatically balances over- and
under-fitting to noisy data. Our tool yields exploratory results are similar to
previous ones and we find no indication that the $R$-ratio was previously
mismodeled. Whilst we advance some aspects of modeling the $R$-ratio and
develop new tools for doing so, a competitive estimate of the HVP contributions
requires domain-specific expertise and a carefully curated database of
measurements.
