Errors when estimating criteria for censored Gaussian model

I have run a Gaussian model on data with an upper-censored response. ESS seems good and there are no warnings with the fit, but when I run loo, bayes_R2, or loo_R2, I get the following error

Error : vector memory exhausted (limit reached?)
Error: Something went wrong (see the error message above). Perhaps you transformed numeric variables to factors or vice versa within the model formula? If yes, please convert your variables beforehand. Or did you set a predictor variable to NA?

And this is despite setting nsamples to a fairly low number. The code I am running is

mdl_tapped_pulse_tap_vel_red <-
      tap_vel | cens(censoring) ~
        cue * 
        (tap_l_1 + 
           perf_num_sc + repetition_sc + 
           N_sc + mean_IOI_sc + 
           CQ_sc + balance_sc +
           proj_cent_sc + height_sc + 
           duple_triple +
           Markov2_sc + edge_sc),
      decomp = "QR"),
    data = WF_rhythm_data_tapped,
    family = gaussian(),
    prior = c(set_prior("student_t(3, 0, 1)", class = "Intercept"),
              set_prior("student_t(3, 0, 1)", class = "b")),
    save_pars = save_pars(all = TRUE),
    file = "mdl_tapped_pulse_tap_vel_red")

mdl_tapped_pulse_tap_vel_red <-
    c("loo", "bayes_R2", "loo_R2"),
    nsamples = 500
bayes_R2(mdl_tapped_pulse_tap_vel_red )

I am running brms 2.14.0.

I am not sure how to proceed or if this is a bug. Thanks in advance for any suggestions.

OK, problem solved – I just needed to reduce nsamples a bit more!