R Under development (unstable) (2026-05-15 r90061 ucrt) -- "Unsuffered Consequences"
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> library(testthat)
> library(bvarnet)
> 
> 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 
  11.84    1.18   13.01