Updating reference

paper/paper.bib

@@ -77,7 +77,20 @@ @article{beta-binomial
  volume = {29},
  year = {1973}
 }
-
+@article{Murphy_reliability,
+ ISSN = {00359254, 14679876},
+ URL = {http://www.jstor.org/stable/2346866},
+ abstract = {This paper briefly describes some results of operational and experimental programmes in the United States involving subjective probability forecasts of precipitation occurrence and of maximum and minimum temperatures. These results indicate that weather forecasters can formulate such forecasts in a reliable manner.},
+ author = {Allan H. Murphy and Robert L. Winkler},
+ journal = {Journal of the Royal Statistical Society. Series C (Applied Statistics)},
+ number = {1},
+ pages = {41--47},
+ publisher = {[Royal Statistical Society, Oxford University Press]},
+ title = {Reliability of Subjective Probability Forecasts of Precipitation and Temperature},
+ urldate = {2024-11-05},
+ volume = {26},
+ year = {1977}
+}
 @article{Prevalence_HORSCH,
 title = {Prevalence Scaling: Applications to an Intelligent Workstation for the Diagnosis of Breast Cancer},
 journal = {Academic Radiology},
@@ -130,16 +143,6 @@ @article{gneiting2007strictly
   year={2007},
   publisher={Taylor \& Francis}
 }
-@article{steyerberg2010assessing,
-  title={Assessing the performance of prediction models: a framework for traditional and novel measures},
-  author={Steyerberg, Ewout W and Vickers, Andrew J and Cook, Nancy R and Gerds, Thomas and Gonen, Mithat and Obuchowski, Nancy and Pencina, Michael J and Kattan, Michael W},
-  journal={Epidemiology},
-  volume={21},
-  number={1},
-  pages={128--138},
-  year={2010},
-  publisher={LWW}
-}
 
 @book{McCullagh:1989,
   added-at = {2010-01-10T01:48:50.000+0100},

paper/paper.md

@@ -59,7 +59,7 @@ In the `calzone` package, we provide a set of functions and classes for calibrat
 
 ## Reliability Diagram
 
-The reliability diagram (also referred to as a calibration plot) is a graphical representation of the calibration of a classification model [@Brocker_reldia;steyerberg2010assessing]. It groups the predicted probabilities into bins and plots the mean predicted probability against the empirical frequency in each bin. The reliability diagram can be used to assess the calibration of the model and to identify any systematic errors in the predictions. In addition, `calzone` gives the option to also plot the confidence interval of the empirical frequency in each bin. The confidence intervals are calculated using Wilson's score interval [@wilson_interval]. We provide example data in the `example_data` folder which are simulated using a beta-binomial distribution [@beta-binomial]. The predicted probabilities are sampled from a beta distribution and the true labels are assigned by performing Bernoulli trials with the sampled probabilities. Users can generate simulated data using the `fake_binary_data_generator` class in the `utils` module.
+The reliability diagram (also referred to as a calibration plot) is a graphical representation of the calibration of a classification model [@Murphy_reliability;@Brocker_reldia]. It groups the predicted probabilities into bins and plots the mean predicted probability against the empirical frequency in each bin. The reliability diagram can be used to assess the calibration of the model and to identify any systematic errors in the predictions. In addition, `calzone` gives the option to also plot the confidence interval of the empirical frequency in each bin. The confidence intervals are calculated using Wilson's score interval [@wilson_interval]. We provide example data in the `example_data` folder which are simulated using a beta-binomial distribution [@beta-binomial]. The predicted probabilities are sampled from a beta distribution and the true labels are assigned by performing Bernoulli trials with the sampled probabilities. Users can generate simulated data using the `fake_binary_data_generator` class in the `utils` module.
 
 ```python
 from calzone.utils import reliability_diagram