Running ‘testthat.R’ [496s/478s]
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: 1.54 seconds (Warm-up)
                 0.63 seconds (Sampling)
                 2.17 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: 1.95 seconds (Warm-up)
                 0.55 seconds (Sampling)
                 2.5 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)
                 0.01 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 = 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)  ──────────────────────
  write error, closing pipe to the master
  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: loo::relative_eff(x = likfun, chain_id = chain_id, data = args$data, draws = args$draws, 
         cores = cores, ...)
  9: relative_eff.function(x = likfun, chain_id = chain_id, data = args$data, draws = args$draws, 
         cores = cores, ...)
  10: parallel::mclapply(X = seq_len(N), FUN = function(i) {
         val_i <- f_i(data_i = data[i, , drop = FALSE], draws = draws, ...)
         relative_eff.default(as.vector(val_i), chain_id = chain_id)
     }, mc.cores = cores)
  11: lapply(seq_len(cores), inner.do)
  12: FUN(X[[i]], ...)
  13: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
  
  ── 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)
                 0.04 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 = 14
             adapt_window = 76
             term_buffer = 10
  
  
   Elapsed Time: 0.02 seconds (Warm-up)
                 0.04 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)
                 0.02 seconds (Sampling)
                 0.03 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 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: 0.01 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: 0.27 seconds (Warm-up)
                 0.18 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 = 7
             adapt_window = 38
             term_buffer = 5
  
  
   Elapsed Time: 0.26 seconds (Warm-up)
                 0.19 seconds (Sampling)
                 0.45 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: 1.3 seconds (Warm-up)
                 0.87 seconds (Sampling)
                 2.17 seconds (Total)
  
  
  Gradient evaluation took 0.01 seconds
  1000 transitions using 10 leapfrog steps per transition would take 100 seconds.
  Adjust your expectations accordingly!
  
  
  
   Elapsed Time: 2.32 seconds (Warm-up)
                 0.88 seconds (Sampling)
                 3.2 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: 0.01 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: 0.02 seconds (Warm-up)
                 0.01 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 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 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)
                 0.28 seconds (Sampling)
                 0.57 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 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 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 / 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 0.01 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 0.01 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 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)
  
  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: 0.01 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: 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 0.01 seconds
  1000 transitions using 10 leapfrog steps per transition would take 100 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: 0.01 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: 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: 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)
                 0.03 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 = 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)
                 0.04 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: 0.07 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/RtmpovaiMO/RLIBS_51312274ddf/BH/include/boost/config.hpp:39:0,
                   from /tmp/RtmpovaiMO/RLIBS_51312274ddf/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 file314d50816bdc.cpp:5:
  /tmp/RtmpovaiMO/RLIBS_51312274ddf/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 0.01 seconds
  1000 transitions using 10 leapfrog steps per transition would take 100 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)
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  Gradient evaluation took 0 seconds
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  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)
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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!
  
  
  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)
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  Gradient evaluation took 0 seconds
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  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)
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  Gradient evaluation took 0 seconds
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  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)
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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!
  
  
  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
  
  
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  Gradient evaluation took 0 seconds
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  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
  
  
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  Gradient evaluation took 0 seconds
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  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)
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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!
  
  
  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)
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  Gradient evaluation took 0 seconds
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  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)
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  Gradient evaluation took 0 seconds
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  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
  
  
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  Gradient evaluation took 0 seconds
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  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)
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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!
  
  
  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: 0.98 seconds (Warm-up)
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  Gradient evaluation took 0 seconds
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  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)
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  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
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             term_buffer = 5
  
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  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:
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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:
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  SAMPLING FOR MODEL 'count' NOW (CHAIN 1).
  
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  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:
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  SAMPLING FOR MODEL 'count' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
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  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:
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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:
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             term_buffer = 5
  
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  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
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  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
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  WARNING: No variance estimation is
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  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
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  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
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  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)
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  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)
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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 1).
  
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  SAMPLING FOR MODEL 'binomial' NOW (CHAIN 1).
  
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  SAMPLING FOR MODEL 'binomial' NOW (CHAIN 2).
  
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  ------------------------------------------------------------
  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
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  Adjust your expectations accordingly!
  
  
  Begin eta 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).
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
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   Elapsed Time: 2.35 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 3).
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
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  WARNING: No variance estimation is
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  ══ 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