R Under development (unstable) (2024-02-28 r85999 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > 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 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 3 GIRFs for 2 structural shocks, each based on 4 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 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 2 cores to estimate 50 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 8 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 50 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 1 cores for 1 estimations rounds... Optimizing with a genetic algorithm... Optimizing with a variable metric algorithm... Filtering inappropriate estimates... Calculating approximate standard errors... Finished! Using 1 cores for 1 estimations rounds... Optimizing with a genetic algorithm... Optimizing with a variable metric algorithm... Filtering inappropriate estimates... Calculating approximate standard errors... Finished! Using 1 cores for 1 estimations rounds... Optimizing with a genetic algorithm... Optimizing with a variable metric algorithm... Filtering inappropriate estimates... 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 Filtering inappropriate estimates... 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 Filtering inappropriate estimates... Calculating approximate standard errors... Finished! Using 1 cores for 2 estimations rounds... Optimizing with a genetic algorithm... Optimizing with a variable metric algorithm... Filtering inappropriate estimates... Calculating approximate standard errors... Finished! Using 1 cores for 2 bootstrap replications... [ FAIL 0 | WARN 0 | SKIP 0 | PASS 1862 ] > > proc.time() user system elapsed 44.68 2.89 127.39