Update app.py

app.py

@@ -12,15 +12,23 @@ def Phi(z):
 def phi(z):
     return norm.pdf(z)
 
-
-def F_BCNOLLN(y1, y2, params):
-    z1 = (y1 - mu_1) / sigma_1
-    z2 = (y2 - mu_2) / sigma_2
-    term = (Phi(z1)*params["alpha_1"] / H(z1, params["alpha_1"], params["beta_1"]))*(-params["lambda_val"]) + \
-           (Phi(z2)*params["alpha_2"] / H(z2, params["alpha_2"], params["beta_2"]))*(-params["lambda_val"]) - 1
-    return term**(-1/params["lambda_val"])
-
-
+# Define the BCNOLLN CDF based on the provided formula
+def F_BCNOLLN(y1, y2, mu1, sigma1, alpha1, beta1, mu2, sigma2, alpha2, beta2, lambd):
+    # Convert y1, y2 to z-scores
+    z1 = (y1 - mu1) / sigma1
+    z2 = (y2 - mu2) / sigma2
+    
+    # Compute H functions
+    H1_z1 = Phi(z1)**alpha1 + (1 - Phi(z1))**beta1
+    H2_z2 = Phi(z2)**alpha2 + (1 - Phi(z2))**beta2
+    
+    # Compute the BCNOLLN CDF
+    term1 = (Phi(z1)*alpha1 / H1_z1)**(-lambd)
+    term2 = (Phi(z2)*alpha2 / H2_z2)**(-lambd)
+    term = term1 + term2 - 1
+    cdf = term**(-1/lambd)
+    
+    return cdf
 
 def f_BCNOLLN(y1, y2, mu1, sigma1, alpha1, beta1, mu2, sigma2, alpha2, beta2, lambd):
     z1 = (y1 - mu1) / sigma1