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+# report.brms
+
+    Code
+      report(model, verbose = FALSE)
+    Message
+      Start sampling
+      Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 1 Exception: normal_id_glm_lpdf: Scale vector is 0, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 1 
+      Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 2 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 2 
+      Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 2 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 2 
+      Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 3 
+      Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 3 
+      Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 3 
+      Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 3 
+    Output
+      We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
+      of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as student_t
+      (location = 19.20, scale = 5.40) distributions. The model's explanatory power
+      is substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      model:
+      
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
+      probability of being negative (< 0), 100.00% of being significant (< -0.30),
+      and 100.00% of being large (< -1.81). The estimation successfully converged
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
+      
+      Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
+      framework, we report the median of the posterior distribution and its 95% CI
+      (Highest Density Interval), along the probability of direction (pd), the
+      probability of significance and the probability of being large. The thresholds
+      beyond which the effect is considered as significant (i.e., non-negligible) and
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner,
+      2017)., We fitted a Bayesian linear model (estimated using MCMC sampling with 4
+      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as uniform
+      (location = , scale = ) distributions. The model's explanatory power is
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      model:
+      
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
+      probability of being negative (< 0), 100.00% of being significant (< -0.30),
+      and 100.00% of being large (< -1.81). The estimation successfully converged
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
+      
+      Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
+      framework, we report the median of the posterior distribution and its 95% CI
+      (Highest Density Interval), along the probability of direction (pd), the
+      probability of significance and the probability of being large. The thresholds
+      beyond which the effect is considered as significant (i.e., non-negligible) and
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner,
+      2017)., We fitted a Bayesian linear model (estimated using MCMC sampling with 4
+      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as uniform
+      (location = , scale = ) distributions. The model's explanatory power is
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      model:
+      
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
+      probability of being negative (< 0), 100.00% of being significant (< -0.30),
+      and 100.00% of being large (< -1.81). The estimation successfully converged
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
+      
+      Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
+      framework, we report the median of the posterior distribution and its 95% CI
+      (Highest Density Interval), along the probability of direction (pd), the
+      probability of significance and the probability of being large. The thresholds
+      beyond which the effect is considered as significant (i.e., non-negligible) and
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
+      and We fitted a Bayesian linear model (estimated using MCMC sampling with 4
+      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as student_t
+      (location = 0.00, scale = 5.40) distributions. The model's explanatory power is
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      model:
+      
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
+      probability of being negative (< 0), 100.00% of being significant (< -0.30),
+      and 100.00% of being large (< -1.81). The estimation successfully converged
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
+      
+      Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
+      framework, we report the median of the posterior distribution and its 95% CI
+      (Highest Density Interval), along the probability of direction (pd), the
+      probability of significance and the probability of being large. The thresholds
+      beyond which the effect is considered as significant (i.e., non-negligible) and
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
+