diff --git a/src/gmacs.tpl b/src/gmacs.tpl
index 72e44770..eebb2d6e 100644
--- a/src/gmacs.tpl
+++ b/src/gmacs.tpl
@@ -788,6 +788,9 @@ PRELIMINARY_CALCS_SECTION
 		}
 	}
 
+
+	
+
 PROCEDURE_SECTION
 	// Initialize model parameters
 	initialize_model_parameters();
diff --git a/src/include/nloglike.h b/src/include/nloglike.h
index f0ec73c4..407df832 100644
--- a/src/include/nloglike.h
+++ b/src/include/nloglike.h
@@ -5,42 +5,7 @@
 
 #define TINY     1.e-08
 
-namespace likelihoods
-{
-
-class nloglike
-{
-private:
-	int r1;
-	int r2;
-	int c1;
-	int c2;
-
-	ivector jmin;
-	ivector jmax;
-
-	dmatrix m_O;
-	dmatrix m_Or;
-	dmatrix m_residual;
-
-	dvar_matrix m_P;
-	dvar_matrix m_Pr;
-
-public:
-	~nloglike();
-	nloglike(const dmatrix& _O, const dvar_matrix& _P);
-
-	void tail_compression();
-	dvariable multinomial(const dvector& dSampleSize);
-	dvariable dmultinom(const dvector& x, const dvar_vector& p);
-
-	dvariable dmvlogistic();
-
-	dmatrix residuals(const dvector& dSampleSize);
-	
-};
 
-} // namespace
 
 
 
diff --git a/src/lib/nloglike.cpp b/src/lib/nloglike.cpp
index 6eaeaeba..55956605 100644
--- a/src/lib/nloglike.cpp
+++ b/src/lib/nloglike.cpp
@@ -5,167 +5,167 @@
 #if defined _WIN32 || defined _WIN64
 	#include "include\nloglike.h"
 #endif
-using namespace likelihoods;
-
-
-nloglike::nloglike(const dmatrix& _O, const dvar_matrix& _P)
-:m_O(_O),m_P(_P)
-{
-	r1 = m_O.rowmin();
-	r2 = m_O.rowmax();
-	c1 = m_O.colmin();
-	c2 = m_O.colmax();
-	tail_compression();
-	m_residual.allocate(r1,r2,c1,c2);
-}
-
-nloglike::~nloglike()
-{
-
-}
-
-  /**
-   * @brief Multivariate logistic likelihood
-   * @details This is a modified version of the dmvlogistic negative log likelihood
-		where proportions at age less than minp are pooled into the consecutive 
-		age-classes.  See last paragraph in Appendix A of Richards, Schnute and
-		Olsen 1997. 
-   * @return negative log likelihood.
-   */
-dvariable nloglike::dmvlogistic()
-{
+// using namespace likelihoods;
+
+
+// nloglike::nloglike(const dmatrix& _O, const dvar_matrix& _P)
+// :m_O(_O),m_P(_P)
+// {
+// 	r1 = m_O.rowmin();
+// 	r2 = m_O.rowmax();
+// 	c1 = m_O.colmin();
+// 	c2 = m_O.colmax();
+// 	tail_compression();
+// 	m_residual.allocate(r1,r2,c1,c2);
+// }
+
+// nloglike::~nloglike()
+// {
+
+// }
+
+//   /**
+//    * @brief Multivariate logistic likelihood
+//    * @details This is a modified version of the dmvlogistic negative log likelihood
+// 		where proportions at age less than minp are pooled into the consecutive 
+// 		age-classes.  See last paragraph in Appendix A of Richards, Schnute and
+// 		Olsen 1997. 
+//    * @return negative log likelihood.
+//    */
+// dvariable nloglike::dmvlogistic()
+// {
 	
-	int n = 0;
-	dvariable nll = 0;
-	dvar_matrix nu;
-	nu.allocate(m_Pr);
-	nu.initialize();
+// 	int n = 0;
+// 	dvariable nll = 0;
+// 	dvar_matrix nu;
+// 	nu.allocate(m_Pr);
+// 	nu.initialize();
 	
 
-	for(int i = r1; i <= r2; i++ )
-	{
-		if(min(m_Pr(i))==0) {cout<<"Deal with zeros"<<endl; exit(1);}
-		nu(i)  = log(m_Or(i)) - log(m_Pr(i));
-		nu(i) -= mean(nu(i));
-		n += size_count(nu(i)) -1;
-	}
-	dvariable tau2 = 1/n * norm2(nu);
-	nll = n * log(tau2+0.001);
-	return nll;
-}
-
-/**
- * @brief Multinomial likleihood for composition data.
- * @details [long description]
- * 
- * @param dSampleSize The assumed sample size in the multinomial distribution.
- * @return returns the negative log likelihood.
- */	
-dvariable nloglike::multinomial(const dvector& dSampleSize)
-{
-	if( dSampleSize.indexmin() != m_Or.rowmin() || dSampleSize.indexmax() != m_Or.rowmax() )
-	{
-		cerr<<"Sample size index do not match row index in\
-		       observed size composition matrix."<<endl;
-		ad_exit(1);
-	}
-	RETURN_ARRAYS_INCREMENT();
-	dvariable nll = 0;
-	double tiny = 1.e-14;
-	for(int i = r1; i <= r2; i++ )
-	{
-		dvector o = m_Or(i) + tiny;
-		dvar_vector p = m_Pr(i) + tiny;
-		o /= sum(o);
-		p /= sum(p);
+// 	for(int i = r1; i <= r2; i++ )
+// 	{
+// 		if(min(m_Pr(i))==0) {cout<<"Deal with zeros"<<endl; exit(1);}
+// 		nu(i)  = log(m_Or(i)) - log(m_Pr(i));
+// 		nu(i) -= mean(nu(i));
+// 		n += size_count(nu(i)) -1;
+// 	}
+// 	dvariable tau2 = 1/n * norm2(nu);
+// 	nll = n * log(tau2+0.001);
+// 	return nll;
+// }
+
+// /**
+//  * @brief Multinomial likleihood for composition data.
+//  * @details [long description]
+//  * 
+//  * @param dSampleSize The assumed sample size in the multinomial distribution.
+//  * @return returns the negative log likelihood.
+//  */	
+// dvariable nloglike::multinomial(const dvector& dSampleSize)
+// {
+// 	if( dSampleSize.indexmin() != m_Or.rowmin() || dSampleSize.indexmax() != m_Or.rowmax() )
+// 	{
+// 		cerr<<"Sample size index do not match row index in\
+// 		       observed size composition matrix."<<endl;
+// 		ad_exit(1);
+// 	}
+// 	RETURN_ARRAYS_INCREMENT();
+// 	dvariable nll = 0;
+// 	double tiny = 1.e-14;
+// 	for(int i = r1; i <= r2; i++ )
+// 	{
+// 		dvector o = m_Or(i) + tiny;
+// 		dvar_vector p = m_Pr(i) + tiny;
+// 		o /= sum(o);
+// 		p /= sum(p);
 		
-		dvector n = dSampleSize(i) * o;
-		nll += dmultinom(n,p);
-	}
-	RETURN_ARRAYS_DECREMENT();
-	return nll;
-}
-
-
-dvariable nloglike::dmultinom(const dvector& x, const dvar_vector& p)
-{
-	if(x.indexmin() != p.indexmin() || x.indexmax() != p.indexmax())
-	{
-		cerr << "Index bounds do not macth in"
-		" dmultinom(const dvector& x, const dvar_vector& p)\n";
-		ad_exit(1);
-	}
+// 		dvector n = dSampleSize(i) * o;
+// 		nll += dmultinom(n,p);
+// 	}
+// 	RETURN_ARRAYS_DECREMENT();
+// 	return nll;
+// }
+
+
+// dvariable nloglike::dmultinom(const dvector& x, const dvar_vector& p)
+// {
+// 	if(x.indexmin() != p.indexmin() || x.indexmax() != p.indexmax())
+// 	{
+// 		cerr << "Index bounds do not macth in"
+// 		" dmultinom(const dvector& x, const dvar_vector& p)\n";
+// 		ad_exit(1);
+// 	}
 	
-	double n=sum(x);
-	return -gammln(n+1.)+sum(gammln(x+1.))-x*log(p/sum(p));
-}
-
-dmatrix nloglike::residuals(const dvector& dSampleSize)
-{
-	m_residual.initialize();
-	for(int i = r1; i <= r2; i++ )
-	{
-		dvector var = value(elem_prod(m_Pr(i),1.0-m_Pr(i)) / dSampleSize(i));
-		dvector res = elem_div(m_Or(i)-value(m_Pr(i)),sqrt(var + 0.01/(c2-c1)));
-		m_residual(i)(jmin(i),jmax(i)) = res;
-	}
-	return (m_residual);
-}
-
-/**
- * @brief Tail compression
- * @details This algorithim compresses the tails of a size composition matrix, such that
- * there are no zeros at the tails of the size composition data that would interfear
- * with the likelihood calculation.  Note that the data that is less than pmin 
- * is pooled (cululative sum) into the tails of a ragged object.
- */
-void nloglike::tail_compression()
-{
+// 	double n=sum(x);
+// 	return -gammln(n+1.)+sum(gammln(x+1.))-x*log(p/sum(p));
+// }
+
+// dmatrix nloglike::residuals(const dvector& dSampleSize)
+// {
+// 	m_residual.initialize();
+// 	for(int i = r1; i <= r2; i++ )
+// 	{
+// 		dvector var = value(elem_prod(m_Pr(i),1.0-m_Pr(i)) / dSampleSize(i));
+// 		dvector res = elem_div(m_Or(i)-value(m_Pr(i)),sqrt(var + 0.01/(c2-c1)));
+// 		m_residual(i)(jmin(i),jmax(i)) = res;
+// 	}
+// 	return (m_residual);
+// }
+
+// /**
+//  * @brief Tail compression
+//  * @details This algorithim compresses the tails of a size composition matrix, such that
+//  * there are no zeros at the tails of the size composition data that would interfear
+//  * with the likelihood calculation.  Note that the data that is less than pmin 
+//  * is pooled (cululative sum) into the tails of a ragged object.
+//  */
+// void nloglike::tail_compression()
+// {
 	
-	double pmin = 0.001;
+// 	double pmin = 0.001;
 	
 
-	jmin.allocate(r1,r2);
-	jmax.allocate(r1,r2);
+// 	jmin.allocate(r1,r2);
+// 	jmax.allocate(r1,r2);
 
-	for(int i = r1; i <= r2; i++ )
-	{
+// 	for(int i = r1; i <= r2; i++ )
+// 	{
 		
-		dvector o = m_O(i);
-
-		jmin(i) = c1+1;		// index for min column
-		jmax(i) = c2;			// index for max column
-		dvector cumsum = o/sum(o);
-		for(int j = c1+1; j <= c2; j++ )
-		{
-			 cumsum(j) += cumsum(j-1);
-			 cumsum(j) <= pmin ? jmin(i)++ : NULL;
-			 j != c2 ? 1.0 - cumsum(j) < pmin ? jmax(i)-- : NULL : NULL;
-		}
-
-	}
-
-	// now allocate ragged arrays
-	m_Or.allocate(r1,r2,jmin,jmax);
-	m_Pr.allocate(r1,r2,jmin,jmax);
-	m_Or.initialize();
-	m_Pr.initialize();
-
-	// fill ragged arrays
-	for(int i = r1; i <= r2; i++ )
-	{
-		dvector cso     = m_O(i)/sum(m_O(i));
-		dvar_vector csp = m_P(i)/sum(m_P(i));
-		m_Or(i)(jmin(i),jmax(i)) = cso(jmin(i),jmax(i));
-		m_Pr(i)(jmin(i),jmax(i)) = csp(jmin(i),jmax(i));
-
-		// add cumulative sum to tails.
-		m_Or(i)(jmin(i)) = sum(cso(c1,jmin(i)));
-		m_Or(i)(jmax(i)) = sum(cso(jmax(i),c2));
-		m_Pr(i)(jmin(i)) = sum(csp(c1,jmin(i)));
-		m_Pr(i)(jmax(i)) = sum(csp(jmax(i),c2));
-
-	//cout<<jmin(i)<<" "<<jmax(i)<<" "<<m_Or(i)<<endl;
-	}
+// 		dvector o = m_O(i);
+
+// 		jmin(i) = c1+1;		// index for min column
+// 		jmax(i) = c2;			// index for max column
+// 		dvector cumsum = o/sum(o);
+// 		for(int j = c1+1; j <= c2; j++ )
+// 		{
+// 			 cumsum(j) += cumsum(j-1);
+// 			 cumsum(j) <= pmin ? jmin(i)++ : NULL;
+// 			 j != c2 ? 1.0 - cumsum(j) < pmin ? jmax(i)-- : NULL : NULL;
+// 		}
+
+// 	}
+
+// 	// now allocate ragged arrays
+// 	m_Or.allocate(r1,r2,jmin,jmax);
+// 	m_Pr.allocate(r1,r2,jmin,jmax);
+// 	m_Or.initialize();
+// 	m_Pr.initialize();
+
+// 	// fill ragged arrays
+// 	for(int i = r1; i <= r2; i++ )
+// 	{
+// 		dvector cso     = m_O(i)/sum(m_O(i));
+// 		dvar_vector csp = m_P(i)/sum(m_P(i));
+// 		m_Or(i)(jmin(i),jmax(i)) = cso(jmin(i),jmax(i));
+// 		m_Pr(i)(jmin(i),jmax(i)) = csp(jmin(i),jmax(i));
+
+// 		// add cumulative sum to tails.
+// 		m_Or(i)(jmin(i)) = sum(cso(c1,jmin(i)));
+// 		m_Or(i)(jmax(i)) = sum(cso(jmax(i),c2));
+// 		m_Pr(i)(jmin(i)) = sum(csp(c1,jmin(i)));
+// 		m_Pr(i)(jmax(i)) = sum(csp(jmax(i),c2));
+
+// 	//cout<<jmin(i)<<" "<<jmax(i)<<" "<<m_Or(i)<<endl;
+// 	}
 	
-}
+// }