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print.waic output is ok

Code
  print(waic1)
Output
  
  Computed from 1000 by 32 log-likelihood matrix.
  
            Estimate  SE
  elpd_waic    -83.5 4.3
  p_waic         3.3 1.1
  waic         167.1 8.5
  
  3 (9.4%) p_waic estimates greater than 0.4. We recommend trying loo instead.

print.psis_loo and print.psis output ok

Code
  print(psis1)
Output
  Computed from 1000 by 32 log-weights matrix.
  MCSE and ESS estimates assume independent draws (r_eff=1).
  
  All Pareto k estimates are good (k < 0.67).
  See help('pareto-k-diagnostic') for details.

Code
  print(loo1)
Output
  
  Computed from 1000 by 32 log-likelihood matrix.
  
           Estimate  SE
  elpd_loo    -83.6 4.3
  p_loo         3.3 1.2
  looic       167.2 8.6
  ------
  MCSE of elpd_loo is 0.1.
  MCSE and ESS estimates assume independent draws (r_eff=1).
  
  All Pareto k estimates are good (k < 0.67).
  See help('pareto-k-diagnostic') for details.

Code
  print(loo1_r_eff)
Output
  
  Computed from 1000 by 32 log-likelihood matrix.
  
           Estimate  SE
  elpd_loo    -83.6 4.3
  p_loo         3.3 1.2
  looic       167.2 8.6
  ------
  MCSE of elpd_loo is 0.1.
  MCSE and ESS estimates assume MCMC draws (r_eff in [0.6, 1.0]).
  
  All Pareto k estimates are good (k < 0.67).
  See help('pareto-k-diagnostic') for details.

mcse_loo extractor gives correct value

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