Last updated on 2018-06-20 01:50:30 CEST.

Flavor | Version | T_{install} | T_{check} | T_{total} | Status | Flags |
---|---|---|---|---|---|---|

r-devel-linux-x86_64-debian-clang | 2.17.4 | 976.23 | 668.96 | 1645.19 | NOTE | |

r-devel-linux-x86_64-debian-gcc | 2.17.4 | 750.65 | 487.98 | 1238.63 | NOTE | |

r-devel-linux-x86_64-fedora-clang | 2.17.4 | 2440.50 | WARN | |||

r-devel-linux-x86_64-fedora-gcc | 2.17.4 | 2398.00 | NOTE | |||

r-devel-windows-ix86+x86_64 | 2.17.4 | 1872.00 | 347.00 | 2219.00 | NOTE | --no-examples --no-tests --no-vignettes |

r-patched-linux-x86_64 | 2.17.4 | 994.05 | 631.20 | 1625.25 | NOTE | |

r-patched-solaris-x86 | 2.17.4 | 2138.40 | ERROR | |||

r-release-linux-x86_64 | 2.17.4 | 958.53 | 622.82 | 1581.35 | NOTE | |

r-release-windows-ix86+x86_64 | 2.17.4 | 1560.00 | 369.00 | 1929.00 | NOTE | --no-examples --no-tests --no-vignettes |

r-release-osx-x86_64 | 2.17.4 | ERROR | ||||

r-oldrel-windows-ix86+x86_64 | 2.17.4 | 1403.00 | 249.00 | 1652.00 | NOTE | --no-examples --no-tests --no-vignettes |

r-oldrel-osx-x86_64 | 2.17.4 | WARN |

Version: 2.17.4

Check: for GNU extensions in Makefiles

Result: NOTE

GNU make is a SystemRequirements.

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-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-oldrel-osx-x86_64

Version: 2.17.4

Check: whether package can be installed

Result: WARN

Found the following significant warnings:

:481:10: warning: 'long long' is a C++11 extension [-Wc++11-long-long] #pragma clang diagnostic pop

: warning: 'long long' is a C++11 extension [-Wc++11-long-long]1307535010540395uLL

:477:10: warning: 'long long' is a C++11 extension [-Wc++11-long-long]

Flavor: r-devel-linux-x86_64-fedora-clang

Version: 2.17.4

Check: installed package size

Result: NOTE

installed size is 157.4Mb

sub-directories of 1Mb or more:

R 1.4Mb

doc 2.9Mb

libs 152.5Mb

Flavors: r-devel-linux-x86_64-fedora-clang, r-patched-solaris-x86, r-oldrel-osx-x86_64

Version: 2.17.4

Flags: --no-examples --no-tests --no-vignettes

Check: installed package size

Result: NOTE

installed size is 22.1Mb

sub-directories of 1Mb or more:

R 1.4Mb

doc 2.8Mb

libs 17.3Mb

Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 2.17.4

Flags: --no-examples --no-tests --no-vignettes

Check: for GNU extensions in Makefiles

Result: NOTE

GNU make is a SystemRequirements.

Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 2.17.4

Check: tests

Result: ERROR

Running ‘testthat.R’ [499s/486s]

Running the tests in ‘tests/testthat.R’ failed.

Complete output:

> # Part of the rstanarm package for estimating model parameters

> # Copyright (C) 2015 Trustees of Columbia University

> #

> # This program is free software; you can redistribute it and/or

> # modify it under the terms of the GNU General Public License

> # as published by the Free Software Foundation; either version 3

> # of the License, or (at your option) any later version.

> #

> # This program is distributed in the hope that it will be useful,

> # but WITHOUT ANY WARRANTY; without even the implied warranty of

> # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

> # GNU General Public License for more details.

> #

> # You should have received a copy of the GNU General Public License

> # along with this program; if not, write to the Free Software

> # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.

>

> library(testthat)

> library(rstanarm)

Loading required package: Rcpp

rstanarm (Version 2.17.4, packaged: 2018-04-13 01:51:52 UTC)

- Do not expect the default priors to remain the same in future rstanarm versions.

Thus, R scripts should specify priors explicitly, even if they are just the defaults.

- For execution on a local, multicore CPU with excess RAM we recommend calling

options(mc.cores = parallel::detectCores())

- Plotting theme set to bayesplot::theme_default().

> Sys.unsetenv("R_TESTS")

> # options(error = function() traceback(2))

> example(example_model)

exmpl_> example_model <-

exmpl_+ stan_glmer(cbind(incidence, size - incidence) ~ size + period + (1|herd),

exmpl_+ data = lme4::cbpp, family = binomial, QR = TRUE,

exmpl_+ # this next line is only to keep the example small in size!

exmpl_+ chains = 2, cores = 1, seed = 12345, iter = 500)

SAMPLING FOR MODEL 'binomial' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Iteration: 1 / 500 [ 0%] (Warmup)

Iteration: 50 / 500 [ 10%] (Warmup)

Iteration: 100 / 500 [ 20%] (Warmup)

Iteration: 150 / 500 [ 30%] (Warmup)

Iteration: 200 / 500 [ 40%] (Warmup)

Iteration: 250 / 500 [ 50%] (Warmup)

Iteration: 251 / 500 [ 50%] (Sampling)

Iteration: 300 / 500 [ 60%] (Sampling)

Iteration: 350 / 500 [ 70%] (Sampling)

Iteration: 400 / 500 [ 80%] (Sampling)

Iteration: 450 / 500 [ 90%] (Sampling)

Iteration: 500 / 500 [100%] (Sampling)

Elapsed Time: 1.56 seconds (Warm-up)

0.64 seconds (Sampling)

2.2 seconds (Total)

SAMPLING FOR MODEL 'binomial' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Iteration: 1 / 500 [ 0%] (Warmup)

Iteration: 50 / 500 [ 10%] (Warmup)

Iteration: 100 / 500 [ 20%] (Warmup)

Iteration: 150 / 500 [ 30%] (Warmup)

Iteration: 200 / 500 [ 40%] (Warmup)

Iteration: 250 / 500 [ 50%] (Warmup)

Iteration: 251 / 500 [ 50%] (Sampling)

Iteration: 300 / 500 [ 60%] (Sampling)

Iteration: 350 / 500 [ 70%] (Sampling)

Iteration: 400 / 500 [ 80%] (Sampling)

Iteration: 450 / 500 [ 90%] (Sampling)

Iteration: 500 / 500 [100%] (Sampling)

Elapsed Time: 1.97 seconds (Warm-up)

0.56 seconds (Sampling)

2.53 seconds (Total)

exmpl_> example_model

stan_glmer

family: binomial [logit]

formula: cbind(incidence, size - incidence) ~ size + period + (1 | herd)

observations: 56

------

Median MAD_SD

(Intercept) -1.5 0.7

size 0.0 0.0

period2 -1.0 0.3

period3 -1.1 0.3

period4 -1.6 0.5

Error terms:

Groups Name Std.Dev.

herd (Intercept) 0.76

Num. levels: herd 15

Sample avg. posterior predictive distribution of y:

Median MAD_SD

mean_PPD 1.8 0.2

------

For info on the priors used see help('prior_summary.stanreg').

> if (!grepl("^sparc", R.version$platform))

+ test_check("rstanarm", invert = TRUE,

+ filter = if (Sys.getenv("NOT_CRAN") != "true") "jm|mvmer")

Initial log joint probability = -3395

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

------------------------------------------------------------

EXPERIMENTAL ALGORITHM:

This procedure has not been thoroughly tested and may be unstable

or buggy. The interface is subject to change.

------------------------------------------------------------

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Begin eta adaptation.

Iteration: 1 / 250 [ 0%] (Adaptation)

Iteration: 50 / 250 [ 20%] (Adaptation)

Iteration: 100 / 250 [ 40%] (Adaptation)

Iteration: 150 / 250 [ 60%] (Adaptation)

Iteration: 200 / 250 [ 80%] (Adaptation)

Success! Found best value [eta = 1] earlier than expected.

Begin stochastic gradient ascent.

iter ELBO delta_ELBO_mean delta_ELBO_med notes

Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.

This variational approximation is not guaranteed to be meaningful.

Drawing a sample of size 1000 from the approximate posterior...

COMPLETED.

------------------------------------------------------------

EXPERIMENTAL ALGORITHM:

This procedure has not been thoroughly tested and may be unstable

or buggy. The interface is subject to change.

------------------------------------------------------------

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Begin eta adaptation.

Iteration: 1 / 250 [ 0%] (Adaptation)

Iteration: 50 / 250 [ 20%] (Adaptation)

Iteration: 100 / 250 [ 40%] (Adaptation)

Iteration: 150 / 250 [ 60%] (Adaptation)

Success! Found best value [eta = 10] earlier than expected.

Begin stochastic gradient ascent.

iter ELBO delta_ELBO_mean delta_ELBO_med notes

Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.

This variational approximation is not guaranteed to be meaningful.

Drawing a sample of size 1000 from the approximate posterior...

COMPLETED.

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.02 seconds (Warm-up)

0.02 seconds (Sampling)

0.04 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0.04 seconds (Sampling)

0.05 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0.01 seconds (Sampling)

0.02 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0.06 seconds (Sampling)

0.07 seconds (Total)

── 1. Error: loo issues errors/warnings (@test_loo.R#90) ──────────────────────

subscript out of bounds

1: expect_warning(loo(example_model, cores = LOO.CORES, k_threshold = 2), "Setting 'k_threshold' > 1 is not recommended") at testthat/test_loo.R:90

2: quasi_capture(enquo(object), capture_warnings, label = label)

3: capture(act$val <- eval_bare(get_expr(quo), get_env(quo)))

4: withCallingHandlers(code, warning = function(condition) {

out$push(condition)

invokeRestart("muffleWarning")

})

5: eval_bare(get_expr(quo), get_env(quo))

6: loo(example_model, cores = LOO.CORES, k_threshold = 2)

7: loo.stanreg(example_model, cores = LOO.CORES, k_threshold = 2)

8: suppressWarnings(loo.function(llfun, data = args$data, draws = args$draws, r_eff = r_eff,

..., cores = cores, save_psis = save_psis))

9: withCallingHandlers(expr, warning = function(w) invokeRestart("muffleWarning"))

10: loo.function(llfun, data = args$data, draws = args$draws, r_eff = r_eff, ..., cores = cores,

save_psis = save_psis)

11: lapply(psis_list, "[[", "pointwise")

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 14

adapt_window = 76

term_buffer = 10

Elapsed Time: 0.05 seconds (Warm-up)

0.03 seconds (Sampling)

0.08 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 14

adapt_window = 76

term_buffer = 10

Elapsed Time: 0.03 seconds (Warm-up)

0.03 seconds (Sampling)

0.06 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 14

adapt_window = 76

term_buffer = 10

Elapsed Time: 0.03 seconds (Warm-up)

0.03 seconds (Sampling)

0.06 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 14

adapt_window = 76

term_buffer = 10

Elapsed Time: 0.03 seconds (Warm-up)

0.03 seconds (Sampling)

0.06 seconds (Total)

Initial log joint probability = -306.809

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0.01 seconds (Sampling)

0.02 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0.06 seconds (Sampling)

0.07 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Elapsed Time: 0.01 seconds (Warm-up)

0.01 seconds (Sampling)

0.02 seconds (Total)

4-fold cross-validation

Estimate SE

elpd_kfold -82.6 4.6

2-fold cross-validation

Estimate SE

elpd_kfold -100.6 9.4

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0 seconds (Sampling)

0.01 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0 seconds (Sampling)

0.01 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0.01 seconds (Sampling)

0.02 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0.01 seconds (Sampling)

0.02 seconds (Total)

Error in new_CppObject_xp(fields$.module, fields$.pointer, ...) :

object 'SEED' not found

Initial log joint probability = -25083.8

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

------------------------------------------------------------

EXPERIMENTAL ALGORITHM:

This procedure has not been thoroughly tested and may be unstable

or buggy. The interface is subject to change.

------------------------------------------------------------

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Begin eta adaptation.

Iteration: 1 / 250 [ 0%] (Adaptation)

Iteration: 50 / 250 [ 20%] (Adaptation)

Iteration: 100 / 250 [ 40%] (Adaptation)

Iteration: 150 / 250 [ 60%] (Adaptation)

Iteration: 200 / 250 [ 80%] (Adaptation)

Success! Found best value [eta = 1] earlier than expected.

Begin stochastic gradient ascent.

iter ELBO delta_ELBO_mean delta_ELBO_med notes

100 -3e+02 1.000 1.000

200 -2e+02 0.977 1.000

300 -1e+02 0.680 0.955

400 -1e+02 0.517 0.955

500 -1e+02 0.429 0.087

600 -1e+02 0.370 0.087

700 -1e+02 0.325 0.080

800 -1e+02 0.290 0.080

900 -1e+02 0.260 0.071

1000 -1e+02 0.238 0.071

1100 -1e+02 0.138 0.060

1200 -1e+02 0.042 0.039

1300 -1e+02 0.035 0.035

1400 -1e+02 0.039 0.039

1500 -1e+02 0.036 0.039

1600 -1e+02 0.030 0.035

1700 -1e+02 0.027 0.031

1800 -1e+02 0.024 0.024

1900 -1e+02 0.022 0.015

2000 -1e+02 0.021 0.015

2100 -1e+02 0.021 0.015

2200 -1e+02 0.025 0.022

2300 -1e+02 0.024 0.022

2400 -1e+02 0.018 0.014

2500 -1e+02 0.015 0.014

2600 -1e+02 0.015 0.015

2700 -1e+02 0.012 0.010

2800 -1e+02 0.015 0.015

2900 -1e+02 0.019 0.018

3000 -1e+02 0.018 0.015

3100 -1e+02 0.020 0.018

3200 -1e+02 0.016 0.015

3300 -1e+02 0.017 0.015

3400 -1e+02 0.018 0.015

3500 -1e+02 0.017 0.010

3600 -1e+02 0.015 0.009 MEDIAN ELBO CONVERGED

Drawing a sample of size 1000 from the approximate posterior...

COMPLETED.

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Iteration: 1 / 100 [ 1%] (Warmup)

Iteration: 10 / 100 [ 10%] (Warmup)

Iteration: 20 / 100 [ 20%] (Warmup)

Iteration: 30 / 100 [ 30%] (Warmup)

Iteration: 40 / 100 [ 40%] (Warmup)

Iteration: 50 / 100 [ 50%] (Warmup)

Iteration: 51 / 100 [ 51%] (Sampling)

Iteration: 60 / 100 [ 60%] (Sampling)

Iteration: 70 / 100 [ 70%] (Sampling)

Iteration: 80 / 100 [ 80%] (Sampling)

Iteration: 90 / 100 [ 90%] (Sampling)

Iteration: 100 / 100 [100%] (Sampling)

Elapsed Time: 0.03 seconds (Warm-up)

0.03 seconds (Sampling)

0.06 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Elapsed Time: 0.28 seconds (Warm-up)

0.18 seconds (Sampling)

0.46 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Elapsed Time: 0.27 seconds (Warm-up)

0.19 seconds (Sampling)

0.46 seconds (Total)

Initial log joint probability = -470.264

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

------------------------------------------------------------

EXPERIMENTAL ALGORITHM:

This procedure has not been thoroughly tested and may be unstable

or buggy. The interface is subject to change.

------------------------------------------------------------

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Begin eta adaptation.

Iteration: 1 / 250 [ 0%] (Adaptation)

Iteration: 50 / 250 [ 20%] (Adaptation)

Iteration: 100 / 250 [ 40%] (Adaptation)

Iteration: 150 / 250 [ 60%] (Adaptation)

Iteration: 200 / 250 [ 80%] (Adaptation)

Success! Found best value [eta = 1] earlier than expected.

Begin stochastic gradient ascent.

iter ELBO delta_ELBO_mean delta_ELBO_med notes

100 -1e+02 1.000 1.000

200 -1e+02 0.549 1.000

300 -1e+02 0.421 0.164

400 -1e+02 0.365 0.197

500 -9e+01 0.299 0.164

600 -9e+01 0.250 0.164

700 -9e+01 0.215 0.097

800 -9e+01 0.189 0.097

900 -9e+01 0.168 0.038

1000 -1e+02 0.157 0.058

1100 -9e+01 0.063 0.058

1200 -9e+01 0.054 0.038

1300 -9e+01 0.038 0.012

1400 -9e+01 0.020 0.012

1500 -9e+01 0.018 0.012

1600 -9e+01 0.018 0.012

1700 -9e+01 0.020 0.012

1800 -9e+01 0.019 0.012

1900 -9e+01 0.020 0.012

2000 -9e+01 0.014 0.012

2100 -9e+01 0.008 0.009 MEAN ELBO CONVERGED MEDIAN ELBO CONVERGED

Drawing a sample of size 1000 from the approximate posterior...

COMPLETED.

------------------------------------------------------------

EXPERIMENTAL ALGORITHM:

This procedure has not been thoroughly tested and may be unstable

or buggy. The interface is subject to change.

------------------------------------------------------------

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Begin eta adaptation.

Iteration: 1 / 250 [ 0%] (Adaptation)

Iteration: 50 / 250 [ 20%] (Adaptation)

Iteration: 100 / 250 [ 40%] (Adaptation)

Iteration: 150 / 250 [ 60%] (Adaptation)

Iteration: 200 / 250 [ 80%] (Adaptation)

Success! Found best value [eta = 1] earlier than expected.

Begin stochastic gradient ascent.

iter ELBO delta_ELBO_mean delta_ELBO_med notes

100 -4e+02 1.000 1.000

200 -2e+02 1.286 1.573

300 -1e+02 0.937 1.000

400 -1e+02 0.721 1.000

500 -1e+02 0.577 0.237

600 -1e+02 0.497 0.237

700 -1e+02 0.448 0.154

800 -1e+02 0.394 0.154

900 -1e+02 0.355 0.093

1000 -1e+02 0.328 0.093

1100 -1e+02 0.228 0.081

1200 -1e+02 0.072 0.073

1300 -9e+01 0.051 0.047

1400 -9e+01 0.045 0.031

1500 -1e+02 0.051 0.047

1600 -9e+01 0.049 0.047

1700 -9e+01 0.034 0.031

1800 -1e+02 0.034 0.031

1900 -9e+01 0.034 0.031

2000 -1e+02 0.031 0.031

2100 -9e+01 0.035 0.037

2200 -9e+01 0.036 0.037

2300 -1e+02 0.041 0.042

2400 -9e+01 0.049 0.057

2500 -9e+01 0.043 0.042

2600 -9e+01 0.037 0.037

2700 -9e+01 0.037 0.037

2800 -1e+02 0.041 0.042

2900 -9e+01 0.042 0.052

3000 -1e+02 0.039 0.037

3100 -9e+01 0.036 0.024

3200 -9e+01 0.036 0.024

3300 -9e+01 0.027 0.014

3400 -9e+01 0.018 0.011

3500 -1e+02 0.022 0.014

3600 -9e+01 0.024 0.024

3700 -9e+01 0.026 0.024

3800 -9e+01 0.020 0.016

3900 -9e+01 0.016 0.014

4000 -1e+02 0.018 0.014

4100 -9e+01 0.020 0.016

4200 -9e+01 0.020 0.017

4300 -9e+01 0.021 0.017

4400 -9e+01 0.023 0.018

4500 -9e+01 0.022 0.018

4600 -9e+01 0.022 0.018

4700 -9e+01 0.023 0.024

4800 -9e+01 0.023 0.024

4900 -9e+01 0.022 0.024

5000 -9e+01 0.019 0.018

5100 -9e+01 0.019 0.018

5200 -9e+01 0.018 0.018

5300 -9e+01 0.018 0.018

5400 -9e+01 0.016 0.013

5500 -9e+01 0.014 0.013

5600 -9e+01 0.012 0.008 MEDIAN ELBO CONVERGED

Drawing a sample of size 1000 from the approximate posterior...

COMPLETED.

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Elapsed Time: 1.32 seconds (Warm-up)

0.87 seconds (Sampling)

2.19 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Elapsed Time: 2.38 seconds (Warm-up)

0.9 seconds (Sampling)

3.28 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Elapsed Time: 0.03 seconds (Warm-up)

0.02 seconds (Sampling)

0.05 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Elapsed Time: 0.03 seconds (Warm-up)

0.02 seconds (Sampling)

0.05 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0 seconds (Warm-up)

0.01 seconds (Sampling)

0.01 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0.02 seconds (Warm-up)

0.02 seconds (Sampling)

0.04 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0.01 seconds (Warm-up)

0.02 seconds (Sampling)

0.03 seconds (Total)

------------------------------------------------------------

EXPERIMENTAL ALGORITHM:

This procedure has not been thoroughly tested and may be unstable

or buggy. The interface is subject to change.

------------------------------------------------------------

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Begin eta adaptation.

Iteration: 1 / 250 [ 0%] (Adaptation)

Iteration: 50 / 250 [ 20%] (Adaptation)

Iteration: 100 / 250 [ 40%] (Adaptation)

Iteration: 150 / 250 [ 60%] (Adaptation)

Iteration: 200 / 250 [ 80%] (Adaptation)

Success! Found best value [eta = 1] earlier than expected.

Begin stochastic gradient ascent.

iter ELBO delta_ELBO_mean delta_ELBO_med notes

100 -1e+02 1.000 1.000

200 -1e+02 0.589 1.000

300 -9e+01 0.467 0.221

400 -9e+01 0.352 0.221

500 -9e+01 0.282 0.178

600 -9e+01 0.237 0.178

700 -9e+01 0.204 0.012

800 -9e+01 0.179 0.012

900 -9e+01 0.159 0.009 MEDIAN ELBO CONVERGED

Drawing a sample of size 1000 from the approximate posterior...

COMPLETED.

Initial log joint probability = -470.264

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0.01 seconds (Warm-up)

0 seconds (Sampling)

0.01 seconds (Total)

Initial log joint probability = -569.387

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -154193

Exception: beta_lpdf: First shape parameter[4] is 0, but must be > 0! (in 'model_continuous' at line 192)

Exception: beta_lpdf: First shape parameter[4] is 0, but must be > 0! (in 'model_continuous' at line 192)

Exception: beta_lpdf: First shape parameter[14] is 0, but must be > 0! (in 'model_continuous' at line 192)

Exception: beta_lpdf: First shape parameter[47] is 0, but must be > 0! (in 'model_continuous' at line 192)

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -491.938

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -879.578

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -484.693

Exception: beta_lpdf: Second shape parameter[1] is 0, but must be > 0! (in 'model_continuous' at line 185)

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -426.896

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -615.262

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Rejecting initial value:

Error evaluating the log probability at the initial value.

Exception: beta_lpdf: First shape parameter[6] is 0, but must be > 0! (in 'model_continuous' at line 185)

Rejecting initial value:

Error evaluating the log probability at the initial value.

Exception: beta_lpdf: First shape parameter[6] is 0, but must be > 0! (in 'model_continuous' at line 185)

Initial log joint probability = -257.044

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -569.239

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Iteration: 1 / 100 [ 1%] (Warmup)

Iteration: 10 / 100 [ 10%] (Warmup)

Iteration: 20 / 100 [ 20%] (Warmup)

Iteration: 30 / 100 [ 30%] (Warmup)

Iteration: 40 / 100 [ 40%] (Warmup)

Iteration: 50 / 100 [ 50%] (Warmup)

Iteration: 51 / 100 [ 51%] (Sampling)

Iteration: 60 / 100 [ 60%] (Sampling)

Iteration: 70 / 100 [ 70%] (Sampling)

Iteration: 80 / 100 [ 80%] (Sampling)

Iteration: 90 / 100 [ 90%] (Sampling)

Iteration: 100 / 100 [100%] (Sampling)

Elapsed Time: 0.18 seconds (Warm-up)

0.24 seconds (Sampling)

0.42 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Iteration: 1 / 100 [ 1%] (Warmup)

Iteration: 10 / 100 [ 10%] (Warmup)

Iteration: 20 / 100 [ 20%] (Warmup)

Iteration: 30 / 100 [ 30%] (Warmup)

Iteration: 40 / 100 [ 40%] (Warmup)

Iteration: 50 / 100 [ 50%] (Warmup)

Iteration: 51 / 100 [ 51%] (Sampling)

Iteration: 60 / 100 [ 60%] (Sampling)

Iteration: 70 / 100 [ 70%] (Sampling)

Iteration: 80 / 100 [ 80%] (Sampling)

Iteration: 90 / 100 [ 90%] (Sampling)

Iteration: 100 / 100 [100%] (Sampling)

Elapsed Time: 0.29 seconds (Warm-up)

0.27 seconds (Sampling)

0.56 seconds (Total)

Initial log joint probability = -4956.4

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -6610.95

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -4709.91

Exception: beta_lpdf: Second shape parameter[14] is 0, but must be > 0! (in 'model_continuous' at line 192)

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -4551.1

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -5909.47

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Rejecting initial value:

Error evaluating the log probability at the initial value.

Exception: beta_lpdf: First shape parameter[5] is 0, but must be > 0! (in 'model_continuous' at line 192)

Initial log joint probability = -316.539

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -2387.7

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -3331.05

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -2279.49

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -2155.46

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -3306.94

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Rejecting initial value:

Error evaluating the log probability at the initial value.

Exception: beta_lpdf: First shape parameter[9] is 0, but must be > 0! (in 'model_continuous' at line 192)

Initial log joint probability = -188.536

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0.01 seconds

1000 transitions using 10 leapfrog steps per transition would take 100 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Iteration: 1 / 1 [100%] (Sampling)

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0.01 seconds

1000 transitions using 10 leapfrog steps per transition would take 100 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Iteration: 1 / 1 [100%] (Sampling)

Elapsed Time: 0 seconds (Warm-up)

0.01 seconds (Sampling)

0.01 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Rejecting initial value:

Error evaluating the log probability at the initial value.

Exception: beta_lpdf: Second shape parameter[607] is 0, but must be > 0! (in 'model_continuous' at line 192)

Rejecting initial value:

Error evaluating the log probability at the initial value.

Exception: beta_lpdf: Second shape parameter[9] is 0, but must be > 0! (in 'model_continuous' at line 192)

Rejecting initial value:

Error evaluating the log probability at the initial value.

Exception: beta_lpdf: Second shape parameter[3] is 0, but must be > 0! (in 'model_continuous' at line 192)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Iteration: 1 / 1 [100%] (Sampling)

Elapsed Time: 0 seconds (Warm-up)

0.01 seconds (Sampling)

0.01 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0.01 seconds

1000 transitions using 10 leapfrog steps per transition would take 100 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Iteration: 1 / 1 [100%] (Sampling)

Elapsed Time: 0 seconds (Warm-up)

0.01 seconds (Sampling)

0.01 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Iteration: 1 / 1 [100%] (Sampling)

Elapsed Time: 0 seconds (Warm-up)

0.01 seconds (Sampling)

0.01 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0.01 seconds

1000 transitions using 10 leapfrog steps per transition would take 100 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Iteration: 1 / 1 [100%] (Sampling)

Elapsed Time: 0 seconds (Warm-up)

0.01 seconds (Sampling)

0.01 seconds (Total)

Initial log joint probability = -228.234

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0.01 seconds (Warm-up)

0.02 seconds (Sampling)

0.03 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.14 seconds (Warm-up)

0.36 seconds (Sampling)

0.5 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.22 seconds (Warm-up)

0.23 seconds (Sampling)

0.45 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.17 seconds (Warm-up)

0.25 seconds (Sampling)

0.42 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.12 seconds (Warm-up)

0.3 seconds (Sampling)

0.42 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0.01 seconds (Warm-up)

0 seconds (Sampling)

0.01 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.03 seconds (Warm-up)

0.02 seconds (Sampling)

0.05 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.02 seconds (Warm-up)

0.03 seconds (Sampling)

0.05 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.02 seconds (Warm-up)

0.03 seconds (Sampling)

0.05 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 3

adapt_window = 20

term_buffer = 2

Elapsed Time: 0.02 seconds (Warm-up)

0.03 seconds (Sampling)

0.05 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Elapsed Time: 0.07 seconds (Warm-up)

0.08 seconds (Sampling)

0.15 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Elapsed Time: 0.08 seconds (Warm-up)

0.07 seconds (Sampling)

0.15 seconds (Total)

In file included from /tmp/Rtmpz3aWOP/RLIBS_534643ae4d42/BH/include/boost/config.hpp:39:0,

from /tmp/Rtmpz3aWOP/RLIBS_534643ae4d42/BH/include/boost/fusion/support/config.hpp:11,

from /home/ripley/R/Lib32/rstan/include/boost_not_in_BH/boost/fusion/support/unused.hpp:10,

from file2a5177316fd7.cpp:5:

/tmp/Rtmpz3aWOP/RLIBS_534643ae4d42/BH/include/boost/config/compiler/gcc.hpp:186:0: warning: "BOOST_NO_CXX11_RVALUE_REFERENCES" redefined

# define BOOST_NO_CXX11_RVALUE_REFERENCES

^

<command-line>:0:0: note: this is the location of the previous definition

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0 seconds (Warm-up)

0.04 seconds (Sampling)

0.04 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0 seconds (Warm-up)

0.01 seconds (Sampling)

0.01 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0.01 seconds (Sampling)

0.02 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.02 seconds (Warm-up)

0.01 seconds (Sampling)

0.03 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0.01 seconds (Sampling)

0.02 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.02 seconds (Warm-up)

0.01 seconds (Sampling)

0.03 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.01 seconds (Warm-up)

0.01 seconds (Sampling)

0.02 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.02 seconds (Warm-up)

0.01 seconds (Sampling)

0.03 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.35 seconds (Warm-up)

0.29 seconds (Sampling)

0.64 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.17 seconds (Warm-up)

0.04 seconds (Sampling)

0.21 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 1 seconds (Warm-up)

0.63 seconds (Sampling)

1.63 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.14 seconds (Warm-up)

0.19 seconds (Sampling)

0.33 seconds (Total)

Initial log joint probability = -3437.61

Optimization terminated normally:

Convergence detected: relative change in objective function was below tolerance

Initial log joint probability = -3105.9

Optimization terminated normally:

Convergence detected: relative change in objective function was below tolerance

Initial log joint probability = -581.92

Optimization terminated normally:

Convergence detected: relative change in objective function was below tolerance

Initial log joint probability = -233.123

Optimization terminated normally:

Convergence detected: relative change in objective function was below tolerance

Initial log joint probability = -196.099

Optimization terminated normally:

Convergence detected: relative change in objective function was below tolerance

SAMPLING FOR MODEL 'count' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Iteration: 1 / 100 [ 1%] (Warmup)

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Elapsed Time: 0.08 seconds (Warm-up)

0.09 seconds (Sampling)

0.17 seconds (Total)

SAMPLING FOR MODEL 'count' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Iteration: 1 / 100 [ 1%] (Warmup)

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Elapsed Time: 0.08 seconds (Warm-up)

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SAMPLING FOR MODEL 'count' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Iteration: 1 / 100 [ 1%] (Warmup)

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Elapsed Time: 0.06 seconds (Warm-up)

0.05 seconds (Sampling)

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SAMPLING FOR MODEL 'count' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Iteration: 1 / 100 [ 1%] (Warmup)

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Elapsed Time: 0.06 seconds (Warm-up)

0.05 seconds (Sampling)

0.11 seconds (Total)

SAMPLING FOR MODEL 'count' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Iteration: 1 / 100 [ 1%] (Warmup)

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Iteration: 100 / 100 [100%] (Sampling)

Elapsed Time: 0.06 seconds (Warm-up)

0.05 seconds (Sampling)

0.11 seconds (Total)

SAMPLING FOR MODEL 'count' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 7

adapt_window = 38

term_buffer = 5

Iteration: 1 / 100 [ 1%] (Warmup)

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Elapsed Time: 0.07 seconds (Warm-up)

0.04 seconds (Sampling)

0.11 seconds (Total)

Initial log joint probability = -12673.6

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Initial log joint probability = -173239

Optimization terminated normally:

Convergence detected: relative gradient magnitude is below tolerance

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0 seconds (Warm-up)

0 seconds (Sampling)

0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0.01 seconds (Warm-up)

0.02 seconds (Sampling)

0.03 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Elapsed Time: 0 seconds (Warm-up)

0.01 seconds (Sampling)

0.01 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0 seconds (Warm-up)

0.06 seconds (Sampling)

0.06 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: There aren't enough warmup iterations to fit the

three stages of adaptation as currently configured.

Reducing each adaptation stage to 15%/75%/10% of

the given number of warmup iterations:

init_buffer = 2

adapt_window = 16

term_buffer = 2

Elapsed Time: 0.12 seconds (Warm-up)

0.03 seconds (Sampling)

0.15 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Iteration: 1 / 400 [ 0%] (Warmup)

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Elapsed Time: 6.07 seconds (Warm-up)

2.81 seconds (Sampling)

8.88 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Iteration: 1 / 400 [ 0%] (Warmup)

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Elapsed Time: 9.36 seconds (Warm-up)

2.96 seconds (Sampling)

12.32 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

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Elapsed Time: 0 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

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Elapsed Time: 0 seconds (Warm-up)

0.02 seconds (Sampling)

0.02 seconds (Total)

SAMPLING FOR MODEL 'binomial' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Iteration: 1 / 400 [ 0%] (Warmup)

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Elapsed Time: 1.06 seconds (Warm-up)

0.47 seconds (Sampling)

1.53 seconds (Total)

SAMPLING FOR MODEL 'binomial' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Iteration: 1 / 400 [ 0%] (Warmup)

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Elapsed Time: 0.7 seconds (Warm-up)

0.39 seconds (Sampling)

1.09 seconds (Total)

------------------------------------------------------------

EXPERIMENTAL ALGORITHM:

This procedure has not been thoroughly tested and may be unstable

or buggy. The interface is subject to change.

------------------------------------------------------------

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Begin eta adaptation.

Iteration: 1 / 250 [ 0%] (Adaptation)

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Success! Found best value [eta = 1] earlier than expected.

Begin stochastic gradient ascent.

iter ELBO delta_ELBO_mean delta_ELBO_med notes

100 -3e+03 1.000 1.000

200 -3e+03 0.553 1.000

300 -3e+03 0.055 0.106

400 -3e+03 0.004 0.004 MEAN ELBO CONVERGED MEDIAN ELBO CONVERGED

Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.

This variational approximation is not guaranteed to be meaningful.

Drawing a sample of size 1000 from the approximate posterior...

COMPLETED.

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Iteration: 1 / 400 [ 0%] (Warmup)

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Elapsed Time: 1.32 seconds (Warm-up)

0.88 seconds (Sampling)

2.2 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Iteration: 1 / 400 [ 0%] (Warmup)

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Elapsed Time: 2.37 seconds (Warm-up)

0.91 seconds (Sampling)

3.28 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

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Elapsed Time: 0 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Iteration: 1 / 10 [ 10%] (Warmup)

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Elapsed Time: 0 seconds (Warm-up)

0.01 seconds (Sampling)

0.01 seconds (Total)

SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Iteration: 1 / 10 [ 10%] (Warmup)

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Elapsed Time: 0 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

WARNING: No variance estimation is

performed for num_warmup < 20

Iteration: 1 / 10 [ 10%] (Warmup)

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Elapsed Time: 0.01 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Iteration: 1 / 400 [ 0%] (Warmup)

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Elapsed Time: 3.49 seconds (Warm-up)

1.36 seconds (Sampling)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

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Iteration: 1 / 400 [ 0%] (Warmup)

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Elapsed Time: 2.58 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

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Elapsed Time: 3.44 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

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Elapsed Time: 2.57 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0.01 seconds

1000 transitions using 10 leapfrog steps per transition would take 100 seconds.

Adjust your expectations accordingly!

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Elapsed Time: 0.7 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

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Elapsed Time: 0.9 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

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Elapsed Time: 0.69 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

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Iteration: 1 / 400 [ 0%] (Warmup)

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Elapsed Time: 0.88 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

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Elapsed Time: 2.24 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

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Elapsed Time: 1.69 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

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Elapsed Time: 0.71 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

Gradient evaluation took 0 seconds

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Elapsed Time: 0.92 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

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Elapsed Time: 0.87 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

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Elapsed Time: 1.11 seconds (Warm-up)

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).

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SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).

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Gradient evaluation took 0 seconds

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Elapsed Time: 2.51 seconds (Warm-up)

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Gradient evaluation took 0 seconds

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Elapsed Time: 4.11 seconds (Warm-up)

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Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

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Elapsed Time: 13.72 seconds (Warm-up)

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Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

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Elapsed Time: 12.66 seconds (Warm-up)

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Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

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Elapsed Time: 0.31 seconds (Warm-up)

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Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Elapsed Time: 0.4 seconds (Warm-up)

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SAMPLING FOR MODEL 'lm' NOW (CHAIN 1).

Gradient evaluation took 0 seconds

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WARNING: No variance estimation is

performed for num_warmup < 20

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0 seconds (Total)

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Gradient evaluation took 0 seconds

1000 transitions using 10 leapfrog steps per transition would take 0 seconds.

Adjust your expectations accordingly!

Elapsed Time: 4.62 seconds (Warm-up)

3.64 seconds (Sampling)

8.26 seconds (Total)

Elapsed Time: 4.45 seconds (Warm-up)

3.25 seconds (Sampling)

7.7 seconds (Total)

══ testthat results ═══════════════════════════════════════════════════════════

OK: 1857 SKIPPED: 2 FAILED: 1

1. Error: loo issues errors/warnings (@test_loo.R#90)

Error: testthat unit tests failed

Execution halted

Flavor: r-patched-solaris-x86

Version: 2.17.4

Check: re-building of vignette outputs

Result: WARN

Error in re-building vignettes:

...

Warning in engine$weave(file, quiet = quiet, encoding = enc) :

Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.

Quitting from lines 2-15 (./children/SETTINGS-knitr.txt)

Quitting from lines NA-15 (./children/SETTINGS-knitr.txt)

Error: processing vignette 'aov.Rmd' failed with diagnostics:

object 'params' not found

Execution halted

Flavors: r-patched-solaris-x86, r-oldrel-osx-x86_64

Version: 2.17.4

Check: whether package can be installed

Result: ERROR

Installation failed.

Flavor: r-release-osx-x86_64