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Last updated on 2026-07-21 23:58:24 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.0 | 122.69 | 151.99 | 274.68 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.1.2 | 116.59 | 151.53 | 268.12 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.1.2 | 99.00 | 183.10 | 282.10 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 0.1.2 | 125.00 | 140.50 | 265.50 | OK | |
| r-devel-windows-x86_64 | 0.1.0 | 143.00 | 193.00 | 336.00 | ERROR | |
| r-patched-linux-x86_64 | 0.1.0 | 139.36 | 163.77 | 303.13 | ERROR | |
| r-release-linux-x86_64 | 0.1.0 | 138.42 | 163.69 | 302.11 | ERROR | |
| r-release-macos-arm64 | 0.1.2 | 27.00 | 50.00 | 77.00 | OK | |
| r-release-macos-x86_64 | 0.1.2 | 97.00 | 363.00 | 460.00 | OK | |
| r-release-windows-x86_64 | 0.1.0 | 158.00 | 189.00 | 347.00 | ERROR | |
| r-oldrel-macos-arm64 | 0.1.2 | 27.00 | 60.00 | 87.00 | OK | |
| r-oldrel-macos-x86_64 | 0.1.2 | 102.00 | 624.00 | 726.00 | OK | |
| r-oldrel-windows-x86_64 | 0.1.0 | 195.00 | 245.00 | 440.00 | ERROR |
Version: 0.1.0
Check: DESCRIPTION meta-information
Result: NOTE
Missing dependency on R >= 4.1.0 because package code uses the pipe
|> or function shorthand \(...) syntax added in R 4.1.0.
File(s) using such syntax:
‘estimate.R’ ‘formula.R’ ‘toy_data.R’
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-windows-x86_64, r-oldrel-windows-x86_64
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in ‘miniLNM-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: beta_mean
> ### Title: LNM Posterior Mean
> ### Aliases: beta_mean
>
> ### ** Examples
>
> example_data <- lnm_data(N = 50, K = 10)
> xy <- dplyr::bind_cols(example_data[c("X", "y")])
> fit <- lnm(
+ starts_with("y") ~ starts_with("x"), xy,
+ iter = 25, output_samples = 25
+ )
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 0.00043 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.3 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.
Error in if (p$diagnostics$pareto_k > 1) { :
missing value where TRUE/FALSE needed
Calls: lnm ... new -> initialize -> initialize -> vb -> vb -> .local
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [9s/10s]
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 0.000291 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.91 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 0.000346 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.46 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 0.000296 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.96 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
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in 'miniLNM-Ex.R' failed
The error most likely occurred in:
> ### Name: beta_mean
> ### Title: LNM Posterior Mean
> ### Aliases: beta_mean
>
> ### ** Examples
>
> example_data <- lnm_data(N = 50, K = 10)
> xy <- dplyr::bind_cols(example_data[c("X", "y")])
> fit <- lnm(
+ starts_with("y") ~ starts_with("x"), xy,
+ iter = 25, output_samples = 25
+ )
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 0.000508 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.08 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.
Error in if (p$diagnostics$pareto_k > 1) { :
missing value where TRUE/FALSE needed
Calls: lnm ... new -> initialize -> initialize -> vb -> vb -> .local
Execution halted
Flavor: r-devel-windows-x86_64
Version: 0.1.0
Check: tests
Result: ERROR
Running 'testthat.R' [9s]
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 0.000423 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.23 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 0.000342 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.42 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 0.000219 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.19 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
Flavor: r-devel-windows-x86_64
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in ‘miniLNM-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: beta_mean
> ### Title: LNM Posterior Mean
> ### Aliases: beta_mean
>
> ### ** Examples
>
> example_data <- lnm_data(N = 50, K = 10)
> xy <- dplyr::bind_cols(example_data[c("X", "y")])
> fit <- lnm(
+ starts_with("y") ~ starts_with("x"), xy,
+ iter = 25, output_samples = 25
+ )
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 0.000448 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.48 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.
Error in if (p$diagnostics$pareto_k > 1) { :
missing value where TRUE/FALSE needed
Calls: lnm ... new -> initialize -> initialize -> vb -> vb -> .local
Execution halted
Flavor: r-patched-linux-x86_64
Version: 0.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [10s/12s]
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 0.000316 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.16 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 0.000338 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.38 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 0.000281 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.81 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
Flavor: r-patched-linux-x86_64
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in ‘miniLNM-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: beta_mean
> ### Title: LNM Posterior Mean
> ### Aliases: beta_mean
>
> ### ** Examples
>
> example_data <- lnm_data(N = 50, K = 10)
> xy <- dplyr::bind_cols(example_data[c("X", "y")])
> fit <- lnm(
+ starts_with("y") ~ starts_with("x"), xy,
+ iter = 25, output_samples = 25
+ )
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 0.000436 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.36 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.
Error in if (p$diagnostics$pareto_k > 1) { :
missing value where TRUE/FALSE needed
Calls: lnm ... new -> initialize -> initialize -> vb -> vb -> .local
Execution halted
Flavor: r-release-linux-x86_64
Version: 0.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [10s/15s]
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 0.000319 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.19 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 0.000265 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.65 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
Flavor: r-release-linux-x86_64
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in 'miniLNM-Ex.R' failed
The error most likely occurred in:
> ### Name: beta_mean
> ### Title: LNM Posterior Mean
> ### Aliases: beta_mean
>
> ### ** Examples
>
> example_data <- lnm_data(N = 50, K = 10)
> xy <- dplyr::bind_cols(example_data[c("X", "y")])
> fit <- lnm(
+ starts_with("y") ~ starts_with("x"), xy,
+ iter = 25, output_samples = 25
+ )
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 0.000386 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.86 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.
Error in if (p$diagnostics$pareto_k > 1) { :
missing value where TRUE/FALSE needed
Calls: lnm ... new -> initialize -> initialize -> vb -> vb -> .local
Execution halted
Flavor: r-release-windows-x86_64
Version: 0.1.0
Check: tests
Result: ERROR
Running 'testthat.R' [10s]
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 0.000493 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.93 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 0.000364 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.64 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 0.000381 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.81 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
Flavor: r-release-windows-x86_64
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in 'miniLNM-Ex.R' failed
The error most likely occurred in:
> ### Name: beta_mean
> ### Title: LNM Posterior Mean
> ### Aliases: beta_mean
>
> ### ** Examples
>
> example_data <- lnm_data(N = 50, K = 10)
> xy <- dplyr::bind_cols(example_data[c("X", "y")])
> fit <- lnm(
+ starts_with("y") ~ starts_with("x"), xy,
+ iter = 25, output_samples = 25
+ )
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 0.00078 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.8 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.
Error in if (p$diagnostics$pareto_k > 1) { :
missing value where TRUE/FALSE needed
Calls: lnm ... new -> initialize -> initialize -> vb -> vb -> .local
Execution halted
Flavor: r-oldrel-windows-x86_64
Version: 0.1.0
Check: tests
Result: ERROR
Running 'testthat.R' [14s]
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 0.000693 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.93 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 0.000547 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.47 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 0.000656 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.56 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
Flavor: r-oldrel-windows-x86_64
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