Running ‘testthat.R’ [68s/87s]
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 5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.5 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.424928 seconds (Warm-up)
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                 0.695524 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 8.6e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 2.14674 seconds (Warm-up)
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                 3.93389 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: 0.054822 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 4.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.063793 seconds (Warm-up)
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                 0.117026 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 5.7e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.57 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 5.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.53 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 0.000316 seconds
  1000 transitions using 10 leapfrog steps per transition would take 3.16 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.662618 seconds (Warm-up)
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                 1.3911 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0.000105 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.05 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.706927 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 0.000148 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.48 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.716752 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: 0.710458 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 5.4e-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: 0.145253 seconds (Warm-up)
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                 0.298096 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
  
  Gradient evaluation took 3.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.144216 seconds (Warm-up)
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                 0.313545 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
  
  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: 0.137428 seconds (Warm-up)
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                 0.272189 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: 0.131833 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 4.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.100947 seconds (Warm-up)
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                 0.197982 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: 0.102311 seconds (Warm-up)
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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!
  
  
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   Elapsed Time: 0.09507 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 2.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.11403 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 6.6e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.66 seconds.
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   Elapsed Time: 0.286515 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 3.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.291439 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 4.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.301869 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 0.000129 seconds
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
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  1000 transitions using 10 leapfrog steps per transition would take 1.34 seconds.
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 5.8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.58 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 3.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.35 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 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 4).
  
  Gradient evaluation took 3.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.34 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 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 6.7e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.67 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 5.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.54 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 5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.5 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 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 5.8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.58 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 6.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.61 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  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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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 3.7e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
  Adjust your expectations accordingly!
  
  
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  .0%
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  .67%
  .100%
  1
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 137 SKIPPED: 0 FAILED: 1
  1. Error: If it works. (@test-get_contrasts.stanreg.R#10) 
  
  Error: testthat unit tests failed
  Execution halted