variable selection for uncorrelated random variables

Hi

I want to fit a Poisson model with uncorrelated random variables and perform projection prediction.
Is it true that this is not yet possible with projpred or can I specify my model in a different way such that I can use uncorrelated random variables?
I also wonder if it matters for projection prediction whether they are specified as correlated or not.

Unexpected number of | characters in group terms. Please contact the package maintainer.

library(brms)
#> Loading required package: Rcpp
#> Warning: package 'Rcpp' was built under R version 4.3.1
#> Loading 'brms' package (version 2.19.0). Useful instructions
#> can be found by typing help('brms'). A more detailed introduction
#> to the package is available through vignette('brms_overview').
#> 
#> Attaching package: 'brms'
#> The following object is masked from 'package:stats':
#> 
#>     ar
library(projpred)
#> Warning: package 'projpred' was built under R version 4.3.1
#> This is projpred version 2.6.0.
#> 
#> Attaching package: 'projpred'
#> The following object is masked from 'package:brms':
#> 
#>     do_call

# simulate data
species <- paste("species", 1:5, sep = "_")
periods <- paste("period", 1:2, sep = "_")
df <- expand.grid(species, periods)

set.seed(123)
lambdas <- c(rgamma(length(species), 0.7, 0.2),
             rgamma(length(species), 0.5, 0.25))

data_list <- lapply(seq_along(df[, 1]), function(i) {
  out <- tidyr::expand_grid(df[i, ], rpois(30, lambdas[i]))
  names(out) <- c("species", "period", "count")
  return(out)
  })
dat_df <- do.call(rbind.data.frame, data_list)

# MCMC parameters
nchains <- 3 # number of chains
niter <- 4000 # number of iterations (incl. burn-in)
burnin <- niter / 4 # number of initial samples to discard (burn-in)
nparallel <- nchains # number of cores used for parallel computing

## Uncorrelated random random variables
# Fit model
fit1 <- brm(bf(count ~ period + (1 + period || species)),
            data = dat_df,
            family = poisson(),
            chains = nchains, warmup = burnin, iter = niter, cores = nparallel,
            control = list(adapt_delta = 0.99,
                           max_treedepth = 12))
#> Compiling Stan program...
#> Start sampling

# Projection prediction
cvvs_fast1 <- cv_varsel(fit1,
                        method = "forward",
                        nclusters_pred = 20, # speed
                        cv_method = "LOO",
                        # only for the sake of speed
                        validate_search = FALSE)
#> Error in FUN(X[[i]], ...): Unexpected number of `|` characters in group terms. Please contact the package maintainer.

^{Created on 2023-07-24 with reprex v2.0.2}

Kind regards,
Ward

Reproduce the issue with the brms Poisson model using (1 + period || species) and the cv_varsel(fit1, method = "forward") entry point. Trace the group-term parsing that emits the unexpected | error, then establish whether uncorrelated random variables should be supported or documented as unsupported, with a regression test for the chosen behavior.