Running ‘testthat.R’ [10s/13s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # This file is part of the standard setup for testthat.
  > # It is recommended that you do not modify it.
  > #
  > # Where should you do additional test configuration?
  > # Learn more about the roles of various files in:
  > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
  > # * https://testthat.r-lib.org/articles/special-files.html
  > 
  > library(testthat)
  > library(miniLNM)
  > 
  > test_check("miniLNM")
  Chain 1: ------------------------------------------------------------
  Chain 1: EXPERIMENTAL ALGORITHM:
  Chain 1:   This procedure has not been thoroughly tested and may be unstable
  Chain 1:   or buggy. The interface is subject to change.
  Chain 1: ------------------------------------------------------------
  Chain 1: 
  Chain 1: 
  Chain 1: 
  Chain 1: Gradient evaluation took 0.000285 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.85 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: Begin eta adaptation.
  Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
  Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
  Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
  Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
  Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
  Chain 1: Success! Found best value [eta = 1] earlier than expected.
  Chain 1: 
  Chain 1: Begin stochastic gradient ascent.
  Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
  Chain 1: This variational approximation is not guaranteed to be meaningful.
  Chain 1: 
  Chain 1: Drawing a sample of size 25 from the approximate posterior... 
  Chain 1: COMPLETED.
  Saving _problems/test-estimate-7.R
  Chain 1: ------------------------------------------------------------
  Chain 1: EXPERIMENTAL ALGORITHM:
  Chain 1:   This procedure has not been thoroughly tested and may be unstable
  Chain 1:   or buggy. The interface is subject to change.
  Chain 1: ------------------------------------------------------------
  Chain 1: 
  Chain 1: 
  Chain 1: 
  Chain 1: Gradient evaluation took 0.000284 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.84 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: Begin eta adaptation.
  Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
  Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
  Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
  Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
  Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
  Chain 1: Success! Found best value [eta = 1] earlier than expected.
  Chain 1: 
  Chain 1: Begin stochastic gradient ascent.
  Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
  Chain 1: This variational approximation is not guaranteed to be meaningful.
  Chain 1: 
  Chain 1: Drawing a sample of size 25 from the approximate posterior... 
  Chain 1: COMPLETED.
  Saving _problems/test-predict-7.R
  Chain 1: ------------------------------------------------------------
  Chain 1: EXPERIMENTAL ALGORITHM:
  Chain 1:   This procedure has not been thoroughly tested and may be unstable
  Chain 1:   or buggy. The interface is subject to change.
  Chain 1: ------------------------------------------------------------
  Chain 1: 
  Chain 1: 
  Chain 1: 
  Chain 1: Gradient evaluation took 0.000361 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.61 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: Begin eta adaptation.
  Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
  Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
  Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
  Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
  Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
  Chain 1: Success! Found best value [eta = 1] earlier than expected.
  Chain 1: 
  Chain 1: Begin stochastic gradient ascent.
  Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
  Chain 1: This variational approximation is not guaranteed to be meaningful.
  Chain 1: 
  Chain 1: Drawing a sample of size 25 from the approximate posterior... 
  Chain 1: COMPLETED.
  Saving _problems/test-sample-7.R
  [ FAIL 3 | WARN 0 | SKIP 0 | PASS 0 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-estimate.R:4:1'): (code run outside of `test_that()`) ──────────
  Error in `if (p$diagnostics$pareto_k > 1) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed
  Backtrace:
      ▆
   1. └─miniLNM::lnm(...) at test-estimate.R:4:1
   2.   ├─methods::new(...)
   3.   │ ├─methods::initialize(value, ...)
   4.   │ └─methods::initialize(value, ...)
   5.   ├─rstan::vb(stanmodels$lnm, data_list, ...)
   6.   └─rstan::vb(stanmodels$lnm, data_list, ...)
   7.     └─rstan (local) .local(object, ...)
  ── Error ('test-predict.R:4:1'): (code run outside of `test_that()`) ───────────
  Error in `if (p$diagnostics$pareto_k > 1) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed
  Backtrace:
      ▆
   1. └─miniLNM::lnm(...) at test-predict.R:4:1
   2.   ├─methods::new(...)
   3.   │ ├─methods::initialize(value, ...)
   4.   │ └─methods::initialize(value, ...)
   5.   ├─rstan::vb(stanmodels$lnm, data_list, ...)
   6.   └─rstan::vb(stanmodels$lnm, data_list, ...)
   7.     └─rstan (local) .local(object, ...)
  ── Error ('test-sample.R:4:1'): (code run outside of `test_that()`) ────────────
  Error in `if (p$diagnostics$pareto_k > 1) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed
  Backtrace:
      ▆
   1. └─miniLNM::lnm(...) at test-sample.R:4:1
   2.   ├─methods::new(...)
   3.   │ ├─methods::initialize(value, ...)
   4.   │ └─methods::initialize(value, ...)
   5.   ├─rstan::vb(stanmodels$lnm, data_list, ...)
   6.   └─rstan::vb(stanmodels$lnm, data_list, ...)
   7.     └─rstan (local) .local(object, ...)
  
  [ FAIL 3 | WARN 0 | SKIP 0 | PASS 0 ]
  Error:
  ! Test failures.
  Execution halted