Running ‘testthat.R’ [72s/98s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(psycho)
  > 
  > test_check("psycho")
  Failed with error:  'there is no package called 'GPArotation''
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
  Rejecting initial value:
    Log probability evaluates to log(0), i.e. negative infinity.
    Stan can't start sampling from this initial value.
  Rejecting initial value:
    Log probability evaluates to log(0), i.e. negative infinity.
    Stan can't start sampling from this initial value.
  
  Gradient evaluation took 0.000193 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.93 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.451211 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0.000127 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.27 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 2.21604 seconds (Warm-up)
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                 4.13496 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 5.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.54 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.067575 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 4.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.068012 seconds (Warm-up)
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                 0.133195 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 5.6e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.56 seconds.
  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     -4e+02             1.000            1.000
     200     -2e+02             1.011            1.021
     300     -2e+02             0.682            1.000
     400     -2e+02             0.511            1.000
     500     -2e+02             0.409            0.024
     600     -2e+02             0.341            0.024
     700     -2e+02             0.293            0.002   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 6.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
  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     -9e+04             1.000            1.000
     200     -6e+02            79.074          157.147
     300     -5e+02            52.759            1.000
     400     -2e+02            39.982            1.651
     500     -2e+02            31.989            1.000
     600     -2e+02            26.660            1.000
     700     -2e+02            22.854            0.130
     800     -2e+02            19.999            0.130
     900     -2e+02            17.778            0.018
    1000     -2e+02            16.000            0.018
    1100     -2e+02            15.902            0.016   MAY BE DIVERGING... INSPECT ELBO
    1200     -2e+02             0.188            0.014
    1300     -2e+02             0.176            0.014
    1400     -2e+02             0.011            0.014
    1500     -2e+02             0.010            0.013   MEAN ELBO CONVERGED
  
  Drawing a sample of size 1000 from the approximate posterior... 
  COMPLETED.
  Initial log joint probability = -100816
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  The Crawford-Howell (1998) t-test suggests no dissociation between test X and test Y (t(5) = 2.10, p = 0.09<b0>). The patient's score on test X is not significantly altered compared to its score on test Y.
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0.000175 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.75 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.750815 seconds (Warm-up)
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                 1.52614 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0.000101 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.01 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.740689 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 0.000107 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.07 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.714991 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 0.000101 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.01 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.744592 seconds (Warm-up)
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  ── 1. Error: If it works. (@test-get_contrasts.stanreg.R#10)  ──────────────────
  number of items to replace is not a multiple of replacement length
  1: psycho::get_contrasts(fit, "Salary") at testthat/test-get_contrasts.stanreg.R:10
  2: get_contrasts.stanreg(fit, "Salary")
  3: fit %>% emmeans::emmeans(formula) %>% coda::as.mcmc() %>% as.matrix() %>% coda::as.mcmc() %>% 
         as.data.frame()
  4: withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
  5: eval(quote(`_fseq`(`_lhs`)), env, env)
  6: eval(quote(`_fseq`(`_lhs`)), env, env)
  7: `_fseq`(`_lhs`)
  8: freduce(value, `_function_list`)
  9: function_list[[i]](value)
  10: as.matrix(.)
  11: as.matrix.mcmc(.)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
  
  Gradient evaluation took 5.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.52 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.163432 seconds (Warm-up)
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                 0.324638 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
  
  Gradient evaluation took 3.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.33 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.146824 seconds (Warm-up)
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                 0.32269 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
  
  Gradient evaluation took 2.8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.146694 seconds (Warm-up)
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                 0.288113 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
  
  Gradient evaluation took 3.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.132414 seconds (Warm-up)
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                 0.266442 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 4.7e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.112075 seconds (Warm-up)
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                 0.213814 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 2.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.105817 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 3.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.32 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.101736 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 2.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.127623 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 6.8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.68 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.318638 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 6.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.62 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.322527 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 5.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.51 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.324928 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 5.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.51 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 0.000208 seconds
  1000 transitions using 10 leapfrog steps per transition would take 2.08 seconds.
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 0.000148 seconds
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 5.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.59 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
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 3.8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.38 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
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 4.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.41 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
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 3.8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.38 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
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0.0001 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1 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
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 7.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.74 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
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 6.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.62 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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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 4.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.41 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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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 7.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.74 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 3.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 3.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 3.8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.38 seconds.
  Adjust your expectations accordingly!
  
  
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  .0%
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  1
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 136 SKIPPED: 0 FAILED: 1
  1. Error: If it works. (@test-get_contrasts.stanreg.R#10) 
  
  Error: testthat unit tests failed
  Execution halted