added exceptions to kfre_person(), included acknowledgements

min_readme.md

@@ -52,7 +52,7 @@ If you use `kfre` in your research or projects, please consider citing it.
       month        = may,
       year         = 2024,
       publisher    = {Zenodo},
-      version      = {0.1.6},
+      version      = {0.1.7},
       doi          = {10.5281/zenodo.11100222},
       url          = {https://doi.org/10.5281/zenodo.11100222}
     }
@@ -66,6 +66,9 @@ The KFRE model developed by Tangri et al. has made significant contributions to
 
 The `kfre` library is based on the risk prediction models developed in the studies referenced below. Please refer to these studies for an in-depth understanding of the kidney failure risk prediction models used within this library.
 
+Special thanks to Panayiotis Petousis, PhD, Obidiugwu Duru, MD, MS, Kenn B. Daratha, PhD, Keith C. Norris, MD, PhD, Katherine R. Tuttle MD, FASN, FACP, FNKF, Susanne B. Nicholas, MD, MPH, PhD, and Alex Bui, PhD. Their exceptional work on end-stage kidney disease has greatly inspired the creation of this library.
+
+
 ## References 
 Sumida, K., Nadkarni, G. N., Grams, M. E., Sang, Y., Ballew, S. H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S. J., Chang, A. R., Cirillo, M., Daratha, K. B., Gansevoort, R. T., Garg, A. X., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C. P., Lash, J., Lee, B. J., Major, R. W., Metzger, M., Miura, K., Naimark, D. M. J., Nelson, R. G., Sawhney, S., Stempniewicz, N., Tang, M., Townsend, R. R., Traynor, J. P., Valdivielso, J. M., Wetzels, J., Polkinghorne, K. R., & Heerspink, H. J. L. (2020). Conversion of urine protein-creatinine ratio or urine dipstick protein to urine albumin-creatinine ratio for use in chronic kidney disease screening and prognosis. *Annals of Internal Medicine*, *173*(6), 426-435. https://doi.org/10.7326/M20-0529
 

src/kfre/main.py

@@ -194,6 +194,30 @@ def kfre_person(
         Returns:
         - float: The computed risk of developing CKD.
         """
+        if age is None:
+            raise ValueError("Must supply a value for age.")
+
+        if is_male is None:
+            raise ValueError("Must specify sex using True or False for is_male.")
+
+        if eGFR is None:
+            raise ValueError("Must supply a value for eGFR.")
+
+        if uACR is None:
+            raise ValueError("Must supply a value for uACR.")
+
+        if years not in [2, 5]:
+            raise ValueError("Value must be 2 or 5 for 2-year risk or 5-year risk.")
+
+        if dm is not None and dm not in [0, 1, True, False]:
+            raise ValueError("The dm parameter must be either 0, 1, True, or False.")
+
+        if htn is not None and htn not in [0, 1, True, False]:
+            raise ValueError("The htn parameter must be either 0, 1, True, or False.")
+
+        if is_north_american is None:
+            raise ValueError("Must specify True or False for is_north_american.")
+
         # Use is_male directly, since it's already a boolean
         # Call the risk prediction function with the parameters
         risk_prediction = risk_pred(