CRAN Package Check Results for Package miniLNM

Last updated on 2026-07-12 21:53:25 CEST.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	0.1.0	115.23	157.59	272.82	ERROR
r-devel-linux-x86_64-debian-gcc	0.1.0	113.56	128.10	241.66	ERROR
r-devel-linux-x86_64-fedora-clang	0.1.0	148.00	252.38	400.38	ERROR
r-devel-linux-x86_64-fedora-gcc	0.1.0	272.00	262.05	534.05	ERROR
r-devel-windows-x86_64	0.1.0	144.00	189.00	333.00	ERROR
r-patched-linux-x86_64	0.1.0	122.16	160.85	283.01	ERROR
r-release-linux-x86_64	0.1.0	127.84	161.62	289.46	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	149.00	199.00	348.00	ERROR
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	204.00	254.00	458.00	ERROR

Check Details

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 0.000431 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.31 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 0.000285 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.85 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. Saving _problems/test-estimate-7.R Chain 1: ------------------------------------------------------------ Chain 1: EXPERIMENTAL ALGORITHM: Chain 1: This procedure has not been thoroughly tested and may be unstable Chain 1: or buggy. The interface is subject to change. Chain 1: ------------------------------------------------------------ Chain 1: Chain 1: Chain 1: Chain 1: Gradient evaluation took 0.000284 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.84 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. Saving _problems/test-predict-7.R Chain 1: ------------------------------------------------------------ Chain 1: EXPERIMENTAL ALGORITHM: Chain 1: This procedure has not been thoroughly tested and may be unstable Chain 1: or buggy. The interface is subject to change. Chain 1: ------------------------------------------------------------ Chain 1: Chain 1: Chain 1: Chain 1: Gradient evaluation took 0.000361 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.61 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. Saving _problems/test-sample-7.R [ FAIL 3 | WARN 0 | SKIP 0 | PASS 0 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-estimate.R:4:1'): (code run outside of `test_that()`) ────────── Error in `if (p$diagnostics$pareto_k > 1) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed Backtrace: ▆ 1. └─miniLNM::lnm(...) at test-estimate.R:4:1 2. ├─methods::new(...) 3. │ ├─methods::initialize(value, ...) 4. │ └─methods::initialize(value, ...) 5. ├─rstan::vb(stanmodels$lnm, data_list, ...) 6. └─rstan::vb(stanmodels$lnm, data_list, ...) 7. └─rstan (local) .local(object, ...) ── Error ('test-predict.R:4:1'): (code run outside of `test_that()`) ─────────── Error in `if (p$diagnostics$pareto_k > 1) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed Backtrace: ▆ 1. └─miniLNM::lnm(...) at test-predict.R:4:1 2. ├─methods::new(...) 3. │ ├─methods::initialize(value, ...) 4. │ └─methods::initialize(value, ...) 5. ├─rstan::vb(stanmodels$lnm, data_list, ...) 6. └─rstan::vb(stanmodels$lnm, data_list, ...) 7. └─rstan (local) .local(object, ...) ── Error ('test-sample.R:4:1'): (code run outside of `test_that()`) ──────────── Error in `if (p$diagnostics$pareto_k > 1) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed Backtrace: ▆ 1. └─miniLNM::lnm(...) at test-sample.R:4:1 2. ├─methods::new(...) 3. │ ├─methods::initialize(value, ...) 4. │ └─methods::initialize(value, ...) 5. ├─rstan::vb(stanmodels$lnm, data_list, ...) 6. └─rstan::vb(stanmodels$lnm, data_list, ...) 7. └─rstan (local) .local(object, ...) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 0 ] Error: ! Test failures. Execution halted 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 0.000388 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.88 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/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 0.000214 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-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 0.000143 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.43 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 0.00013 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.3 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-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 0.00073 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.3 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-fedora-clang

Version: 0.1.0
Check: tests
Result: ERROR Running ‘testthat.R’ [16s/57s] 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 0.000704 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.04 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 0.000367 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.67 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-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 0.000421 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. 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 0.003823 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 38.23 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. 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/16s] 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 0.004942 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 49.42 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-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 0.000459 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.59 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 0.000439 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. 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 0.000712 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.12 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 0.000584 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.84 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: Iteration: 250 / 250 [100%] (Adaptation) Chain 1: Success! Found best value [eta = 0.1]. 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 0.000215 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.15 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 0.000323 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.23 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-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 0.000436 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-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 0.000321 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.21 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. 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 0.000347 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.47 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 0.000292 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.92 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 0.000414 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.14 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. 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/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 0.000382 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.82 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 0.000292 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.92 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 0.00038 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.8 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. 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 0.000591 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.91 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. 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 0.000361 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.61 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. Saving _problems/test-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 0.000367 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.67 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: Iteration: 250 / 250 [100%] (Adaptation) Chain 1: Success! Found best value [eta = 0.1]. 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 0.000348 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.48 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. 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 0.000827 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 8.27 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. 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 0.000718 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.18 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 0.000685 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.85 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-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 0.000722 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.22 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