sampling() fails if an unrecognized option is provided

Summary:

rstan::sampling doesn't seem to reject or ignore invalid options passed through the ... argument.

Description:

If an invalid option is given, it is not ignored but it causes an ugly failure:

> samples <- sampling(model, data=data.input,
                      chains=4, iter=1000, warmup=500, zzz=TRUE)

Error in FUN(X[[i]], ...) :
  trying to get slot "mode" from an object (class "try-error") that is not an S4 object
In addition: Warning message:
In parallel::mclapply(1:chains, FUN = callFun, mc.preschedule = FALSE,  :
  4 function calls resulted in an error

This came up because I have a function that takes a ... argument that gets passed to rstan::sampling, and I had a typo in one of my arguments (which didn't match and got passed to rstan).

Reproducible Steps:

This is enough to see the failure:

model <- stan_model(model_code="
parameters {
  real y;
}
model {
  y ~ normal(0,1);
}
")
samples <- sampling(model, zzz=TRUE)

Expected Output:

Unrecognized options should be ignored and sampling should proceed correctly. It seems that there's code in the optimizing method that checks recognizable names passed through the ... argument, but no such code exists in sampling.

> samples <- optimizing(model, zzz=TRUE)
Error: passing unknown arguments: zzz.

RStan Version:

The version of RStan you are running (e.g., from packageVersion("rstan")): 2.19.2

R Version:

The version of R you are running (e.g., from R.version.string): 3.6.1

Operating System:

Your operating system (e.g., OS X 10.11.3): Ubuntu 18.04.3 LTS

Start by reproducing the failure with rstan::sampling(model, zzz=TRUE), then compare its handling of ... with the behavior of optimizing, which already reports unknown arguments. Trace the sampling entry point and verify that an unrecognized option no longer causes the shown error and that sampling proceeds as expected.