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Last updated on 2026-07-01 00:54:01 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.0 | 122.79 | 158.51 | 281.30 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.1.0 | 114.83 | 123.67 | 238.50 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.1.0 | 84.00 | 148.47 | 232.47 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 0.1.0 | 247.00 | 280.14 | 527.14 | ERROR | |
| r-devel-windows-x86_64 | 0.1.0 | 139.00 | 185.00 | 324.00 | ERROR | |
| r-patched-linux-x86_64 | 0.1.0 | 126.36 | 172.17 | 298.53 | NOTE | |
| r-release-linux-x86_64 | 0.1.0 | ERROR | ||||
| r-release-macos-arm64 | 0.1.0 | 26.00 | 41.00 | 67.00 | NOTE | |
| r-release-macos-x86_64 | 0.1.0 | 84.00 | 280.00 | 364.00 | NOTE | |
| r-release-windows-x86_64 | 0.1.0 | 147.00 | 198.00 | 345.00 | NOTE | |
| r-oldrel-macos-arm64 | 0.1.0 | NOTE | ||||
| r-oldrel-macos-x86_64 | 0.1.0 | 78.00 | 226.00 | 304.00 | NOTE | |
| r-oldrel-windows-x86_64 | 0.1.0 | 191.00 | 248.00 | 439.00 | 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-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-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 0.000439 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.39 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’ [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 0.000228 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.28 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 0.000216 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.16 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-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 0.00023 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.3 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.
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:
> 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 0.00036 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.6 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-gcc
Version: 0.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [7s/8s]
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 0.000189 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.89 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 0.000127 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.27 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 0.000185 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.85 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-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-gcc
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 0.000424 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.24 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-fedora-clang
Version: 0.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’
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 0.00054 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.4 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 0.000437 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.37 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 0.000264 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.64 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-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-fedora-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 0.001765 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 17.65 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.
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-fedora-gcc
Version: 0.1.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [13s/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 0.000784 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.84 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-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 0.000419 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.19 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-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 0.000463 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.63 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-fedora-gcc
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 0.000701 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.01 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' [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 0.000456 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.56 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-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 0.000453 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.53 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 0.000336 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.36 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.
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 0.000421 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.21 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 0.000279 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.79 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 0.000225 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.25 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 0.000214 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.14 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 0.000506 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.06 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' [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 0.000467 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.67 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 0.00062 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.2 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 0.000735 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.35 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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.