More quiet warnings...

src/df1b2-separable/df1b2lp6.cpp

@@ -609,9 +609,9 @@ dvector laplace_approximation_calculator::banded_calculations
       }
       if (initial_df1b2params::separable_flag)
       {
-        dvector scale(1,(int)nvar);   // need to get scale from somewhere
-        /*int check=*/initial_params::stddev_scale(scale,x);
-        dvector sscale=scale(1,Dux(1).indexmax());
+        dvector local_scale(1,(int)nvar);   // need to get scale from somewhere
+        /*int check=*/initial_params::stddev_scale(local_scale,x);
+        dvector sscale=local_scale(1,Dux(1).indexmax());
         for (i=1;i<=usize;i++)
         {
           Dux(i)=elem_prod(Dux(i),sscale);

src/linad99/conjprod.cpp

@@ -264,10 +264,10 @@ void fmmc::fmin(const double& fret, const dvector& _p, const dvector& _gg)
   }
 
   {
-    for (int j=1;j<=n;j++)
+    for (int local_j=1;local_j<=n;local_j++)
     {
-      g[j] = -xi[j];
-      xi[j]=h[j]=g[j];
+      g[local_j] = -xi[local_j];
+      xi[local_j]=h[local_j]=g[local_j];
     }
   }
 
@@ -453,11 +453,11 @@ void fmmc::fmin(const double& fret, const dvector& _p, const dvector& _gg)
 
     this->dgg=this->gg=0.0;
     {
-      for (int j=1;j<=n;j++)
+      for (int local_j=1;local_j<=n;local_j++)
       {
-        this->gg += g[j]*g[j];
-/*      dgg += xi[j]*xi[j];  */
-        this->dgg += (xi[j]+g[j])*xi[j];
+        this->gg += g[local_j]*g[local_j];
+/*      dgg += xi[local_j]*xi[local_j];  */
+        this->dgg += (xi[local_j]+g[local_j])*xi[local_j];
       }
     }
     if (this->gg == 0.0)
@@ -467,10 +467,10 @@ void fmmc::fmin(const double& fret, const dvector& _p, const dvector& _gg)
     }
     this->gam=this->dgg/this->gg;
     {
-      for (int j=1;j<=n;j++)
+      for (int local_j=1;local_j<=n;local_j++)
       {
-        g[j] = -xi[j]; // g seems to hold the negative gradient
-        xi[j]=h[j]=g[j]+this->gam*h[j];
+        g[local_j] = -xi[local_j]; // g seems to hold the negative gradient
+        xi[local_j]=h[local_j]=g[local_j]+this->gam*h[local_j];
       }
     }
 //  if (this->iter <= ITMAX) goto label1000;

src/linad99/df13fun.cpp

@@ -965,15 +965,15 @@ df1_three_matrix choleski_decomp(const df1_three_matrix& MM)
  * Description not yet available.
  * \param
  */
-dvariable& dvariable::operator = (const df1_three_variable& v)
+dvariable& dvariable::operator = (const df1_three_variable& _v)
 {
   const prevariable * px=df1_three_variable::ind_var[0];
   const prevariable * py=df1_three_variable::ind_var[1];
   const prevariable * pz=df1_three_variable::ind_var[2];
-  double  dfx= *v.get_u_x();
-  double  dfy= *v.get_u_y();
-  double  dfz= *v.get_u_z();
-  value(*this)=*v.get_u();
+  double  dfx = *_v.get_u_x();
+  double  dfy = *_v.get_u_y();
+  double  dfz = *_v.get_u_z();
+  value(*this) = *_v.get_u();
 
   gradient_structure::GRAD_STACK1->set_gradient_stack(default_evaluation3ind,
     &(value(*this)),&(value(*px)),dfx,&(value(*py)),dfy,&(value(*pz)),

src/nh99/lmnewton.cpp

@@ -224,7 +224,7 @@ void function_minimizer::limited_memory_quasi_newton(
 
 void function_minimizer::limited_memory_quasi_newton
   (double& f, const independent_variables& _x, int m, int noprintx,
-  int maxfn, double crit)
+  int _maxfn, double crit)
 {
   independent_variables& x = (independent_variables&) _x;
   if (m<=0)
@@ -353,7 +353,7 @@ void function_minimizer::limited_memory_quasi_newton
     goto L50;
   }
   ++icall;
-  if (icall > maxfn)
+  if (icall > _maxfn)
   {
     goto L50;
   }

src/nh99/nuts.cpp

@@ -546,9 +546,9 @@ void function_minimizer::nuts_mcmc_routine(int nmcmc,int iseed0, [[maybe_unused]
   independent_variables _parsaveprime(1,nvar);
 
   // These are used inside NUTS by reference
-  int _nalphaprime, _nprime, _nfevals;
-  double _alphaprime;
-  bool _sprime, _divergent;
+  int _nalphaprime = 0;
+  double _alphaprime = 0.0;
+  bool _sprime;
   int ndivergent=0; // # divergences post-warmup
   int nsamples=0; // total samples, not always nmcmc if duration option used
   // Declare some local variables used below.
@@ -591,7 +591,7 @@ void function_minimizer::nuts_mcmc_routine(int nmcmc,int iseed0, [[maybe_unused]
       epsvec(1)=eps; epsbar(1)=eps; Hbar(1)=0;
     }
     // Generate single NUTS trajectory by repeatedly doubling build_tree
-    _nprime=0; _divergent=0; _nfevals=0;
+    int _nprime=0; bool _divergent=0; int _nfevals=0;
     n=1; s=1; j=0;
     // Reset global ones to initial point of this trajectory
     thetaminus_end=theta; thetaplus_end=theta; thetaprime=theta;

src/nh99/randeff.cpp

@@ -82,23 +82,23 @@ dvariable function_minimizer::random_effects_maximization(const dvar_vector& _x)
   }
   gradient_structure::set_YES_DERIVATIVES();
   // set convergence criterion for this phase
-  dvector convergence_criteria;
-  if (!(!convergence_criteria))
+  dvector local_convergence_criteria;
+  if (!(!local_convergence_criteria))
   {
-    int ind=min(convergence_criteria.indexmax(),
+    int ind=min(local_convergence_criteria.indexmax(),
     initial_params::current_phase);
-    crit=convergence_criteria(ind);
+    crit=local_convergence_criteria(ind);
   }
   if (!ISZERO(_crit))
   {
     crit = _crit;
   }
-  dvector maximum_function_evaluations;
-  if (!(!maximum_function_evaluations) && !maxfn_option)
+  dvector local_maximum_function_evaluations;
+  if (!(!local_maximum_function_evaluations) && !maxfn_option)
   {
-    int ind=min(maximum_function_evaluations.indexmax(),
+    int ind=min(local_maximum_function_evaluations.indexmax(),
       initial_params::current_phase);
-    maxfn= (int) maximum_function_evaluations(ind);
+    maxfn= (int) local_maximum_function_evaluations(ind);
   }
   dvariable vf=0.0;