bmsma

bmsma_model("ols") |>
  bmsma_assign_data(X = Loblolly$age,
                   Y = Loblolly$height,
                   N = nrow(Loblolly)) |>
  bmsma_run()
#> 
#> SAMPLING FOR MODEL 'ols' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.024 seconds (Warm-up)
#> Chain 1:                0.022 seconds (Sampling)
#> Chain 1:                0.046 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'ols' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.024 seconds (Warm-up)
#> Chain 2:                0.023 seconds (Sampling)
#> Chain 2:                0.047 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'ols' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 3e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.027 seconds (Warm-up)
#> Chain 3:                0.022 seconds (Sampling)
#> Chain 3:                0.049 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'ols' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 3e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.025 seconds (Warm-up)
#> Chain 4:                0.024 seconds (Sampling)
#> Chain 4:                0.049 seconds (Total)
#> Chain 4:
#> Inference for Stan model: ols.
#> 4 chains, each with iter=2000; warmup=1000; thin=1; 
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#> 
#>              mean se_mean   sd    2.5%     25%     50%     75%   97.5% n_eff
#> intercept   -1.31    0.02 0.63   -2.59   -1.73   -1.31   -0.86   -0.09  1697
#> beta         2.59    0.00 0.04    2.51    2.56    2.59    2.62    2.67  1649
#> sigma        2.99    0.01 0.24    2.58    2.83    2.98    3.14    3.49  1989
#> lp__      -132.21    0.03 1.21 -135.23 -132.75 -131.91 -131.32 -130.80  1438
#>           Rhat
#> intercept    1
#> beta         1
#> sigma        1
#> lp__         1
#> 
#> Samples were drawn using NUTS(diag_e) at Mon Aug 10 05:50:34 2026.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at 
#> convergence, Rhat=1).