CRAN Package Check Results for Package rstanarm

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

Additional issues

clang-UBSAN

Check Details

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)
     Iteration: 51 / 100 [ 51%] (Sampling)
     Iteration: 100 / 100 [100%] (Sampling)
    
     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)
     Iteration: 51 / 100 [ 51%] (Sampling)
     Iteration: 100 / 100 [100%] (Sampling)
    
     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)
     Iteration: 51 / 100 [ 51%] (Sampling)
     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)
     Iteration: 51 / 100 [ 51%] (Sampling)
     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)
     Iteration: 51 / 100 [ 51%] (Sampling)
     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)
     Iteration: 51 / 100 [ 51%] (Sampling)
     Iteration: 100 / 100 [100%] (Sampling)
    
     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)
     Iteration: 201 / 400 [ 50%] (Sampling)
     Iteration: 400 / 400 [100%] (Sampling)
    
     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)
     Iteration: 201 / 400 [ 50%] (Sampling)
     Iteration: 400 / 400 [100%] (Sampling)
    
     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
    
     Iteration: 1 / 10 [ 10%] (Warmup)
     Iteration: 2 / 10 [ 20%] (Warmup)
     Iteration: 3 / 10 [ 30%] (Warmup)
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     Iteration: 10 / 10 [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 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)
     Iteration: 2 / 10 [ 20%] (Warmup)
     Iteration: 3 / 10 [ 30%] (Warmup)
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     Iteration: 9 / 10 [ 90%] (Sampling)
     Iteration: 10 / 10 [100%] (Sampling)
    
     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)
     Iteration: 201 / 400 [ 50%] (Sampling)
     Iteration: 400 / 400 [100%] (Sampling)
    
     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)
     Iteration: 201 / 400 [ 50%] (Sampling)
     Iteration: 400 / 400 [100%] (Sampling)
    
     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)
     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+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)
     Iteration: 201 / 400 [ 50%] (Sampling)
     Iteration: 400 / 400 [100%] (Sampling)
    
     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)
     Iteration: 201 / 400 [ 50%] (Sampling)
     Iteration: 400 / 400 [100%] (Sampling)
    
     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
    
     Iteration: 1 / 10 [ 10%] (Warmup)
     Iteration: 2 / 10 [ 20%] (Warmup)
     Iteration: 3 / 10 [ 30%] (Warmup)
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     Iteration: 5 / 10 [ 50%] (Warmup)
     Iteration: 6 / 10 [ 60%] (Sampling)
     Iteration: 7 / 10 [ 70%] (Sampling)
     Iteration: 8 / 10 [ 80%] (Sampling)
     Iteration: 9 / 10 [ 90%] (Sampling)
     Iteration: 10 / 10 [100%] (Sampling)
    
     Elapsed Time: 0 seconds (Warm-up)
     0 seconds (Sampling)
     0 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: No variance estimation is
     performed for num_warmup < 20
    
     Iteration: 1 / 10 [ 10%] (Warmup)
     Iteration: 2 / 10 [ 20%] (Warmup)
     Iteration: 3 / 10 [ 30%] (Warmup)
     Iteration: 4 / 10 [ 40%] (Warmup)
     Iteration: 5 / 10 [ 50%] (Warmup)
     Iteration: 6 / 10 [ 60%] (Sampling)
     Iteration: 7 / 10 [ 70%] (Sampling)
     Iteration: 8 / 10 [ 80%] (Sampling)
     Iteration: 9 / 10 [ 90%] (Sampling)
     Iteration: 10 / 10 [100%] (Sampling)
    
     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)
     Iteration: 2 / 10 [ 20%] (Warmup)
     Iteration: 3 / 10 [ 30%] (Warmup)
     Iteration: 4 / 10 [ 40%] (Warmup)
     Iteration: 5 / 10 [ 50%] (Warmup)
     Iteration: 6 / 10 [ 60%] (Sampling)
     Iteration: 7 / 10 [ 70%] (Sampling)
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     Iteration: 9 / 10 [ 90%] (Sampling)
     Iteration: 10 / 10 [100%] (Sampling)
    
     Elapsed Time: 0 seconds (Warm-up)
     0 seconds (Sampling)
     0 seconds (Total)
    
    
     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)
     Iteration: 2 / 10 [ 20%] (Warmup)
     Iteration: 3 / 10 [ 30%] (Warmup)
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     Iteration: 9 / 10 [ 90%] (Sampling)
     Iteration: 10 / 10 [100%] (Sampling)
    
     Elapsed Time: 0.01 seconds (Warm-up)
     0 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!
    
    
     Iteration: 1 / 400 [ 0%] (Warmup)
     Iteration: 201 / 400 [ 50%] (Sampling)
     Iteration: 400 / 400 [100%] (Sampling)
    
     Elapsed Time: 3.49 seconds (Warm-up)
     1.36 seconds (Sampling)
     4.85 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)
     Iteration: 201 / 400 [ 50%] (Sampling)
     Iteration: 400 / 400 [100%] (Sampling)
    
     Elapsed Time: 2.58 seconds (Warm-up)
     1.35 seconds (Sampling)
     3.93 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!
    
    
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     Elapsed Time: 3.44 seconds (Warm-up)
     1.34 seconds (Sampling)
     4.78 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.
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     Elapsed Time: 2.57 seconds (Warm-up)
     1.35 seconds (Sampling)
     3.92 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.
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     Elapsed Time: 0.7 seconds (Warm-up)
     0.35 seconds (Sampling)
     1.05 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.
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     Elapsed Time: 0.9 seconds (Warm-up)
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     1.26 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
    
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     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
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     Elapsed Time: 0.69 seconds (Warm-up)
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     1.04 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.
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     Elapsed Time: 0.88 seconds (Warm-up)
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     1.24 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.
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     Elapsed Time: 2.24 seconds (Warm-up)
     0.88 seconds (Sampling)
     3.12 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.
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     Elapsed Time: 1.69 seconds (Warm-up)
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     2.98 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.
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     Elapsed Time: 0.71 seconds (Warm-up)
     0.34 seconds (Sampling)
     1.05 seconds (Total)
    
    
     SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
    
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     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
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     Elapsed Time: 0.92 seconds (Warm-up)
     0.44 seconds (Sampling)
     1.36 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.
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     Elapsed Time: 0.87 seconds (Warm-up)
     0.33 seconds (Sampling)
     1.2 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.
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     Elapsed Time: 1.11 seconds (Warm-up)
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     1.44 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.
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     Elapsed Time: 2.17 seconds (Warm-up)
     1.33 seconds (Sampling)
     3.5 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.
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     Elapsed Time: 2.1 seconds (Warm-up)
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     3.3 seconds (Total)
    
    
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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: 4.8 seconds (Warm-up)
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     8.54 seconds (Total)
    
    
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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: 4.45 seconds (Warm-up)
     2.22 seconds (Sampling)
     6.67 seconds (Total)
    
    
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     1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
     Adjust your expectations accordingly!
    
    
    
     Elapsed Time: 2.51 seconds (Warm-up)
     2.39 seconds (Sampling)
     4.9 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: 4.11 seconds (Warm-up)
     1.9 seconds (Sampling)
     6.01 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: 13.72 seconds (Warm-up)
     17.13 seconds (Sampling)
     30.85 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: 12.66 seconds (Warm-up)
     11.68 seconds (Sampling)
     24.34 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: 0.31 seconds (Warm-up)
     0.27 seconds (Sampling)
     0.58 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: 0.4 seconds (Warm-up)
     0.3 seconds (Sampling)
     0.7 seconds (Total)
    
    
     SAMPLING FOR MODEL 'lm' 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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     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!
    
    
    
     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