incosnisten \cite

mcmc/MCMCProcessor.h

@@ -79,7 +79,7 @@ class MCMCProcessor {
     /// @param Mute Allow silencing many messages, especially important if we calculate matrix many times
     void MakeCovariance_MP(const bool Mute = false);
     /// @brief Make and Draw SubOptimality
-    /// @cite roberts2009adaptive
+    /// \cite roberts2009adaptive
     void MakeSubOptimality(const int NIntervals = 10);
 
     /// @brief Reset 2D posteriors, in case we would like to calculate in again with different BurnInCut
@@ -287,23 +287,23 @@ class MCMCProcessor {
     /// This function computes the Effective Sample Size (ESS) using the autocorrelations
     /// calculated by AutoCorrelation(). Ensure that the parameter nLags here matches
     /// the number of lags used in AutoCorrelation() to obtain accurate results.
-    /// @cite StanManual
-    /// @cite hanson2008mcmc
-    /// @cite gabry2024visual
+    /// \cite StanManual
+    /// \cite hanson2008mcmc
+    /// \cite gabry2024visual
     inline void CalculateESS(const int nLags, double **LagL);
     /// @brief Get the batched means variance estimation and variable indicating if number of batches is sensible
-    /// @cite chakraborty2019estimating
-    /// @cite rossetti2024batch
+    /// \cite chakraborty2019estimating
+    /// \cite rossetti2024batch
     inline void BatchedAnalysis();
     /// @brief CW: Batched means, literally read from an array and chuck into TH1D
     inline void BatchedMeans();
     /// @brief Geweke Diagnostic based on the methods described by Fang (2014) and Karlsbakk (2011).
-    /// @cite Fang2014GewekeDiagnostics
-    /// @cite karlsbakk2011
+    /// \cite Fang2014GewekeDiagnostics
+    /// \cite karlsbakk2011
     inline void GewekeDiagnostic();
     /// @brief Acceptance Probability
     inline void AcceptanceProbabilities();
-    /// @brief RC: Perform spectral analysis of MCMC based on @cite Dunkley:2004sv
+    /// @brief RC: Perform spectral analysis of MCMC based on \cite Dunkley:2004sv
     inline void PowerSpectrumAnalysis();
 
     /// Name of MCMC file

mcmc/MinuitFit.h

@@ -9,7 +9,7 @@
 #include "Math/Functor.h"
 
 /// @brief Implementation of Minuit fitting algorithm
-/// @cite James:2004xla
+/// \cite James:2004xla
 class MinuitFit : public LikelihoodFit {
  public:
   /// @brief Constructor

mcmc/StatisticalUtils.h

@@ -13,7 +13,7 @@
 #include "manager/manager.h"
 
 // **************************
-/// @brief  KS: Following H. Jeffreys @cite jeffreys1998theory
+/// @brief  KS: Following H. Jeffreys \cite jeffreys1998theory
 /// @param BayesFactor Obtained value of Bayes factor
 inline std::string GetJeffreysScale(const double BayesFactor){
 // **************************
@@ -99,7 +99,7 @@ inline double GetBIC(const double llh, const int data, const int nPars){
 
 // ****************
 /// @brief KS: See 14.3.10 in Numerical Recipes in C
-/// @cite press1992numerical
+/// \cite press1992numerical
 inline double GetNeffective(const int N1, const int N2) {
 // ****************
 
@@ -224,7 +224,7 @@ inline double GetBetaParameter(const double data, const double mc, const double
 
 
 // *********************
-/// @brief Based on @cite roberts2009adaptive
+/// @brief Based on \cite roberts2009adaptive
 /// @param EigenValues Eigen values of covariance matrix
 inline double GetSubOptimality(const std::vector<double>& EigenValues, const int TotalTarameters) {
 // *********************