library(blavaan)

data("PoliticalDemocracy", package = "lavaan")

model <- "
  # latent variable definitions
  dem60 =~ y1   a*y2
  dem65 =~ y5   a*y6

  # regressions
  dem65 ~ dem60

  # residual correlations
  y1 ~~ y5
"

bfit <- blavaan::bsem(model,
                      data = PoliticalDemocracy,
                      n.chains = 1, burnin = 50, sample = 100
)
#> Warning messages:
#> 1: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat 
#> 2: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess 
#> 3: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess 
#> 4: blavaan WARNING: As specified, the theta covariance matrix is neither diagonal nor unrestricted, so the actual prior might differ from the stated prior. See
#>  https://arxiv.org/abs/2301.08667 

blavaan::blavCompare(bfit, bfit)
#> 
#> WAIC estimates: 
#>  object1:  1436.048 
#>  object2:  1436.048 
#> 
#>  ELPD difference & SE: 
#>     0.000    0.000 
#> 
#> LOO estimates: 
#>  object1:  1435.986 
#>  object2:  1435.986 
#> 
#>  ELPD difference & SE: 
#>     0.000    0.000 
#> 
#> Laplace approximation to the log-Bayes factor
#> (experimental; positive values favor object1):       NA