Running ‘testthat.R’ [154s/372s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(gmvarkit)
  > 
  > test_check("gmvarkit")
  Using 2 cores to estimate 2 GIRFs for 2 structural shocks, each based on 2 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate one GIRF for 1 structural shocks, each based on 1 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate 1 GIRFs for 2 structural shocks, each based on 1 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate 1 GIRFs for 2 structural shocks, each based on 1 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate 2 GIRFs for 2 structural shocks, each based on 2 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate one GIRF for 1 structural shocks, each based on 1 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Finished!
  Using 2 cores to estimate 6 GIRFs for 2 structural shocks, each based on 2 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate 5 GIRFs for 2 structural shocks, each based on 2 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate 49 GIRFs for 2 structural shocks, each based on 1 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate 10 GIRFs for 2 structural shocks, each based on 5 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate one GIRF for 2 structural shocks, each based on 6 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate 49 GIRFs for 2 structural shocks, each based on 2 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate 10 GIRFs for 2 structural shocks, each based on 3 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 2 cores to estimate one GIRF for 2 structural shocks, each based on 2 Monte Carlo repetitions. 
  Estimating GIRFs for structural shock 1... 
  Estimating GIRFs for structural shock 2... 
  Finished!
  Using 1 cores for 1 estimations rounds... 
  Optimizing with a genetic algorithm...
  Optimizing with a variable metric algorithm...
  Calculating approximate standard errors...
  Finished!
  Using 1 cores for 1 estimations rounds... 
  Optimizing with a genetic algorithm...
  Optimizing with a variable metric algorithm...
  Calculating approximate standard errors...
  Finished!
  Using 1 cores for 1 estimations rounds... 
  Optimizing with a genetic algorithm...
  Optimizing with a variable metric algorithm...
  Calculating approximate standard errors...
  Finished!
  The log-likelihood of the supplied model:    -247.495 
  Constrained log-likelihood prior estimation: -283.263 
  
  Using 1 cores for 1 estimations rounds... 
  Optimizing with a genetic algorithm...
  Results from the genetic algorithm:
  The lowest loglik:  -249.595 
  The mean loglik:    -249.595 
  The largest loglik: -249.595 
  Optimizing with a variable metric algorithm...
  Results from the variable metric algorithm:
  The lowest loglik:  -247.65 
  The mean loglik:    -247.65 
  The largest loglik: -247.65 
  Calculating approximate standard errors...
  Finished!
  The log-likelihood of the supplied model:    -299.858 
  Constrained log-likelihood prior estimation: -299.86 
  
  Using 1 cores for 1 estimations rounds... 
  Optimizing with a genetic algorithm...
  Results from the genetic algorithm:
  The lowest loglik:  -299.86 
  The mean loglik:    -299.86 
  The largest loglik: -299.86 
  Optimizing with a variable metric algorithm...
  Results from the variable metric algorithm:
  The lowest loglik:  -299.858 
  The mean loglik:    -299.858 
  The largest loglik: -299.858 
  Calculating approximate standard errors...
  Finished!
  Using 1 cores for 2 estimations rounds... 
  Optimizing with a genetic algorithm...
  Optimizing with a variable metric algorithm...
  Calculating approximate standard errors...
  Finished!
  [ FAIL 1 | WARN 0 | SKIP 0 | PASS 1594 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure ('test_argumentChecks.R:611'): check_data works correctly ───────────
  check_data(...) not equal to `gdpdef`.
  Attributes: < Component "class": Lengths (4, 3) differ (string compare on first 3) >
  
  [ FAIL 1 | WARN 0 | SKIP 0 | PASS 1594 ]
  Error: Test failures
  Execution halted