Minor updates to scripts README and outputs

scripts/README.md

@@ -14,7 +14,7 @@ python3 run_bakk_simulation.py -s 500 -c
 # Complete simulation
 python3 run_bakk_simulation_complete.py -s 500
 ```
-All three commands should output the simulation tables from the paper. You can ignore the UserWarnings.
+All three commands should output the simulation tables from the paper. You can ignore  ConvergenceWarnings and UserWarnings.
 
 
 Real-Data Example
@@ -39,4 +39,4 @@ First [download the dataset](https://drive.google.com/file/d/1vdSzzBe7tPxfQ_X-hU
 python3 run_real_example.py --n_repetitions 100 --max_iter 10000
 ```
 
-Running the above may take a while. You can the lower the number of bootstrap repetitions for faster results.
+Running the above may take a while (15 minutes on a Macbook Pro M2 Pro). You can lower the number of bootstrap repetitions for faster results.

scripts/run_bakk_simulation.py

@@ -19,6 +19,7 @@ def main(n_simulations=10, latex=False, covariate=False):
         n_init=1,
         max_iter=250,  # Latent Gold default : 250 EM iterations + 50 NR iterations
         abs_tol=1e-8,  # Latent Gold default : 1e-8
+        progress_bar=0,
     )
 
     # Model-specific arguments

scripts/run_bakk_simulation_complete.py

@@ -19,6 +19,7 @@ def main(n_simulations=10, latex=False, covariate=False):
         n_init=1,
         max_iter=500,
         abs_tol=1e-8,
+        progress_bar=0,
     )
 
     # Model specific arguments

scripts/run_real_example.py

@@ -193,10 +193,10 @@ def fit_and_bootstrap(n_steps, correction, permutation, method_str):
     stats["Z"] = stats["mean"] / stats["std"]
 
     # P-value
-    stats["P(<|t|)"] = 2 * norm.cdf(-np.abs(stats["Z"]))
+    stats["P(>|z|)"] = 2 * norm.cdf(-np.abs(stats["Z"]))
 
     stats[["mean", "std", "Z"]] = stats[["mean", "std", "Z"]].round(2)
-    stats["P(<|t|)"] = stats["P(<|t|)"].round(3)
+    stats["P(>|z|)"] = stats["P(>|z|)"].round(3)
     print("\nTable 10 : Family’s income differences between classes for each method.")
     print(stats)