Specifying and fitting LME model with unstructured error correlation within subject

I'd suggest using control=lmerControl(...) to override the error
(something like check.nobs.vs.nRE="ignore", but you can look it up in
the help page ...). Your residual variance and random-effects
variances will indeed be confounded, and I can't say for sure how it
will affect the Kenward-Roger [sic] degrees of freedom calculation,
but the estimates of the fixed effects and their standard errors
should still be correct.

  Actually, if you want Kenward-Roger, that may be the only option I
can think of (other than switching to SAS or something ...) For
various technical reasons previously described on this list (and in
the lme4 paper), it's not possible to force the residual variance to
zero and remove the confounding (or, in fact, to any specified value).
You _can_ fix the residual variance to a very small value (but not
exactly zero) by setting a prior in blme::blmer(), or you can fit a
model without a residual variance in glmmTMB (using dispformula ~ 0),
but ... these models won't work with lmerTest to give you
degrees-of-freedom calculations, as far as I know.

Specifying and fitting LME model with unstructured error correlation within subject

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