CRAN Package Check Results for Package miniLNM

Last updated on 2026-07-16 05:52:01 CEST.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	0.1.0	124.66	152.57	277.23	ERROR
r-devel-linux-x86_64-debian-gcc	0.1.0	113.61	128.21	241.82	ERROR
r-devel-linux-x86_64-fedora-clang	0.1.0	150.00	247.62	397.62	ERROR
r-devel-linux-x86_64-fedora-gcc	0.1.0	117.00	112.11	229.11	ERROR
r-devel-windows-x86_64	0.1.0	150.00	195.00	345.00	ERROR
r-patched-linux-x86_64	0.1.0	139.36	163.77	303.13	ERROR
r-release-linux-x86_64	0.1.0	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	143.00	186.00	329.00	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	194.00	260.00	454.00	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 0.012936 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 129.36 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-devel-linux-x86_64-debian-clang

Version: 0.1.0
Check: tests
Result: ERROR Running ‘testthat.R’ [9s/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 0.000299 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.99 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 0.00026 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.6 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. 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 0.000315 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.15 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-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 0.000927 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 9.27 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-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 0.000195 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.95 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 0.000126 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.26 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 0.000132 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.32 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 0.000667 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.67 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. 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’ [15s/20s] 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 0.000483 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.83 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 0.000406 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.06 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. 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 0.00056 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.6 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. 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 0.000359 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.59 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. 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’ 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 0.000376 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.76 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 0.000168 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.68 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 0.000172 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.72 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 0.000564 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.64 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. 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 0.00035 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.5 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 0.000368 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.68 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 0.000245 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.45 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. Saving _problems/test-sample-7.R [ FAIL 3 | WARN 0 | SKIP 0 | PASS 0 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-estimate.R:4:1'): (code run outside of `test_that()`) ────────── Error in `if (p$diagnostics$pareto_k > 1) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed Backtrace: ▆ 1. └─miniLNM::lnm(...) at test-estimate.R:4:1 2. ├─methods::new(...) 3. │ ├─methods::initialize(value, ...) 4. │ └─methods::initialize(value, ...) 5. ├─rstan::vb(stanmodels$lnm, data_list, ...) 6. └─rstan::vb(stanmodels$lnm, data_list, ...) 7. └─rstan (local) .local(object, ...) ── Error ('test-predict.R:4:1'): (code run outside of `test_that()`) ─────────── Error in `if (p$diagnostics$pareto_k > 1) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed Backtrace: ▆ 1. └─miniLNM::lnm(...) at test-predict.R:4:1 2. ├─methods::new(...) 3. │ ├─methods::initialize(value, ...) 4. │ └─methods::initialize(value, ...) 5. ├─rstan::vb(stanmodels$lnm, data_list, ...) 6. └─rstan::vb(stanmodels$lnm, data_list, ...) 7. └─rstan (local) .local(object, ...) ── Error ('test-sample.R:4:1'): (code run outside of `test_that()`) ──────────── Error in `if (p$diagnostics$pareto_k > 1) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed Backtrace: ▆ 1. └─miniLNM::lnm(...) at test-sample.R:4:1 2. ├─methods::new(...) 3. │ ├─methods::initialize(value, ...) 4. │ └─methods::initialize(value, ...) 5. ├─rstan::vb(stanmodels$lnm, data_list, ...) 6. └─rstan::vb(stanmodels$lnm, data_list, ...) 7. └─rstan (local) .local(object, ...) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 0 ] Error: ! Test failures. Execution halted Flavor: r-devel-windows-x86_64

Version: 0.1.0
Check: examples
Result: ERROR Running examples in ‘miniLNM-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: beta_mean > ### Title: LNM Posterior Mean > ### Aliases: beta_mean > > ### ** Examples > > example_data <- lnm_data(N = 50, K = 10) > xy <- dplyr::bind_cols(example_data[c("X", "y")]) > fit <- lnm( + starts_with("y") ~ starts_with("x"), xy, + iter = 25, output_samples = 25 + ) Chain 1: ------------------------------------------------------------ Chain 1: EXPERIMENTAL ALGORITHM: Chain 1: This procedure has not been thoroughly tested and may be unstable Chain 1: or buggy. The interface is subject to change. Chain 1: ------------------------------------------------------------ Chain 1: Chain 1: Chain 1: Chain 1: Gradient evaluation took 0.000448 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.48 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. Error in if (p$diagnostics$pareto_k > 1) { : missing value where TRUE/FALSE needed Calls: lnm ... new -> initialize -> initialize -> vb -> vb -> .local Execution halted Flavor: r-patched-linux-x86_64

Version: 0.1.0
Check: tests
Result: ERROR Running ‘testthat.R’ [10s/12s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(miniLNM) > > test_check("miniLNM") Chain 1: ------------------------------------------------------------ Chain 1: EXPERIMENTAL ALGORITHM: Chain 1: This procedure has not been thoroughly tested and may be unstable Chain 1: or buggy. The interface is subject to change. Chain 1: ------------------------------------------------------------ Chain 1: Chain 1: Chain 1: Chain 1: Gradient evaluation took 0.000316 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.16 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. Saving _problems/test-estimate-7.R Chain 1: ------------------------------------------------------------ Chain 1: EXPERIMENTAL ALGORITHM: Chain 1: This procedure has not been thoroughly tested and may be unstable Chain 1: or buggy. The interface is subject to change. Chain 1: ------------------------------------------------------------ Chain 1: Chain 1: Chain 1: Chain 1: Gradient evaluation took 0.000338 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.38 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. Saving _problems/test-predict-7.R Chain 1: ------------------------------------------------------------ Chain 1: EXPERIMENTAL ALGORITHM: Chain 1: This procedure has not been thoroughly tested and may be unstable Chain 1: or buggy. The interface is subject to change. Chain 1: ------------------------------------------------------------ Chain 1: Chain 1: Chain 1: Chain 1: Gradient evaluation took 0.000281 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.81 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. Saving _problems/test-sample-7.R [ FAIL 3 | WARN 0 | SKIP 0 | PASS 0 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-estimate.R:4:1'): (code run outside of `test_that()`) ────────── Error in `if (p$diagnostics$pareto_k > 1) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed Backtrace: ▆ 1. └─miniLNM::lnm(...) at test-estimate.R:4:1 2. ├─methods::new(...) 3. │ ├─methods::initialize(value, ...) 4. │ └─methods::initialize(value, ...) 5. ├─rstan::vb(stanmodels$lnm, data_list, ...) 6. └─rstan::vb(stanmodels$lnm, data_list, ...) 7. └─rstan (local) .local(object, ...) ── Error ('test-predict.R:4:1'): (code run outside of `test_that()`) ─────────── Error in `if (p$diagnostics$pareto_k > 1) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed Backtrace: ▆ 1. └─miniLNM::lnm(...) at test-predict.R:4:1 2. ├─methods::new(...) 3. │ ├─methods::initialize(value, ...) 4. │ └─methods::initialize(value, ...) 5. ├─rstan::vb(stanmodels$lnm, data_list, ...) 6. └─rstan::vb(stanmodels$lnm, data_list, ...) 7. └─rstan (local) .local(object, ...) ── Error ('test-sample.R:4:1'): (code run outside of `test_that()`) ──────────── Error in `if (p$diagnostics$pareto_k > 1) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) { warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed Backtrace: ▆ 1. └─miniLNM::lnm(...) at test-sample.R:4:1 2. ├─methods::new(...) 3. │ ├─methods::initialize(value, ...) 4. │ └─methods::initialize(value, ...) 5. ├─rstan::vb(stanmodels$lnm, data_list, ...) 6. └─rstan::vb(stanmodels$lnm, data_list, ...) 7. └─rstan (local) .local(object, ...) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 0 ] Error: ! Test failures. Execution halted Flavor: r-patched-linux-x86_64

Version: 0.1.0
Check: examples
Result: ERROR Running examples in ‘miniLNM-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: beta_mean > ### Title: LNM Posterior Mean > ### Aliases: beta_mean > > ### ** Examples > > example_data <- lnm_data(N = 50, K = 10) > xy <- dplyr::bind_cols(example_data[c("X", "y")]) > fit <- lnm( + starts_with("y") ~ starts_with("x"), xy, + iter = 25, output_samples = 25 + ) Chain 1: ------------------------------------------------------------ Chain 1: EXPERIMENTAL ALGORITHM: Chain 1: This procedure has not been thoroughly tested and may be unstable Chain 1: or buggy. The interface is subject to change. Chain 1: ------------------------------------------------------------ Chain 1: Chain 1: Chain 1: Chain 1: Gradient evaluation took 0.000414 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 0.000756 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.56 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. 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 0.000354 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.54 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 0.000496 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.96 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. Saving _problems/test-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 0.000352 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.52 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 0.000945 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 9.45 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' [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 0.000486 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.86 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Begin eta adaptation. Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) Chain 1: Success! Found best value [eta = 1] earlier than expected. Chain 1: Chain 1: Begin stochastic gradient ascent. Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. Chain 1: This variational approximation is not guaranteed to be meaningful. Chain 1: Chain 1: Drawing a sample of size 25 from the approximate posterior... Chain 1: COMPLETED. 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 0.000329 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.29 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 0.000636 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.36 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-oldrel-windows-x86_64