I like the Nadaraya-Watson (NW) kernel regressor because it allows discrete windowed linear smoothing on non-uniformly sampled data without explicitly presuming a fit function.
It is a "non-parametric" approach and that means it has a different set of assumptions about the basis than typically "parametric" approaches. It " in finite samples the NW estimator tends to have a smaller variance" than expectation-maximization methods.
Is there an estimator built into LabVIEW that has equivalent or greater performance?
I doubt it. LabVIEW is designed for testing, data acquisition, control, etc. It can do the odd complex, specialized mathematics such as the very interesting types of smoothing you referenced, but as this is not its core technology, it is doubtful that the NI Developers have chosen to add this type of function.
Which isn't to say that you couldn't implement a VI in LabVIEW that does just what you need, particularly if you have an existing algorithm in, say, Matlab to guide you.