R Under development (unstable) (2026-06-12 r90141 ucrt) -- "Unsuffered Consequences"
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> library(testthat)
> library(bvarnet)
bvarnet: the precompiled Stan models are not available (missing: model_gaussian, model_binary, model_ordinal).
This happens when the package is installed from a CRAN/r-universe binary, or from source without CmdStan set up, so the models were never compiled.
Calls to bvar() will fail until the models are compiled.
See the installation instructions at https://github.com/flo1met/bvarnet#installation for how to install cmdstanr + CmdStan and reinstall bvarnet from source.
> 
> test_check("bvarnet")
BVAR Network fit
======================================== 
Family:      bernoulli
Outcomes (p): 2 
Lags (K):     1 
Fixed eff.:   2 
Observations: 10 
Rhat max:    1.001
Divergences: 0
Priors:       beta ~ Normal(0, 1), phi ~ Normal(0, 0.5) (all defaults)
Total time:  5.0 sec
======================================== 
Using default priors for: intercept, beta, phi
Using default priors for: intercept, beta, phi
BVAR Network Summary
================================================== 
Family: bernoulli | p=2 | K=1 | n=10
Rhat max: 1.001 | Divergences: 0

--- Intercept ---
 predictor outcome mean   median q5     q95   rhat  ess_bulk ess_tail
 Intercept y_1     -0.040 -0.100 -2.416 1.901 1.001 3000     2800    
 Intercept y_2      0.049  0.027 -1.367 1.529 1.001 3000     2800    


--- Fixed Effect ---
 predictor outcome mean   median q5     q95   rhat  ess_bulk ess_tail
 x_1       y_1      0.080  0.281 -1.113 1.402 1.001 3000     2800    
 x_1       y_2     -0.258 -0.421 -1.497 1.206 1.001 3000     2800    


--- Autoregressive ---
 predictor outcome mean   median q5     q95   rhat  ess_bulk ess_tail
 lag1_y_1  y_1      0.032  0.157 -1.456 1.445 1.001 3000     2800    
 lag1_y_2  y_2     -0.062 -0.046 -1.627 1.104 1.001 3000     2800    


--- Cross-lagged ---
 predictor outcome mean   median q5     q95   rhat  ess_bulk ess_tail
 lag1_y_2  y_1     -0.094 -0.201 -1.288 1.505 1.001 3000     2800    
 lag1_y_1  y_2      0.090  0.109 -1.404 1.314 1.001 3000     2800    


==================================================
Use extract_param() or extract_param(fit, type = "...") for the full parameter table.
Use extract_network_matrix() for the temporal network matrix.
bvarnet: 1 row(s) removed (na_action = 'listwise', skip_lag = FALSE). 7 rows remain.
[ FAIL 0 | WARN 237 | SKIP 0 | PASS 933 ]

[ FAIL 0 | WARN 237 | SKIP 0 | PASS 933 ]
> 
> proc.time()
   user  system elapsed 
  19.43    2.79   22.39