Update insert_rows() (and _cols()) to two-argument signatures

src/fastGrplars.cpp

@@ -250,7 +250,7 @@ uvec fastGrplars(const mat& x, const vec& y, const uword& sMax,
         	}
 			// update correlations (adjustment for unequal group size is taken
         	// care of by update formula)
-			r.insert_rows(k, 1, false);	// do not initialize new memory
+			r.insert_rows(k, 1);	// do not initialize new memory
 			r(k) = (r(k-1) - gamma * a) / sigma;
  			corY.shed_row(whichMin);
 			corU.shed_row(whichMin);
@@ -265,7 +265,7 @@ uvec fastGrplars(const mat& x, const vec& y, const uword& sMax,
 			}
         }
         // update active set
-		active.insert_rows(k, 1, false);	// do not initialize new memory
+		active.insert_rows(k, 1);	// do not initialize new memory
 		active(k) = inactive(whichMin);
 		// update inactive set
 		inactive.shed_row(whichMin);

src/fastLars.cpp

@@ -171,7 +171,7 @@ uvec fastLars(const mat& x, const vec& y, const uword& sMax,
         double minGammaMinus, minGammaPlus, gamma;
         minGammaMinus = gammaMinus.min(whichMinus);
         minGammaPlus = gammaPlus.min(whichPlus);
-		signs.insert_rows(k, 1, false);	// do not initialize new memory
+		signs.insert_rows(k, 1);	// do not initialize new memory
         if(minGammaMinus < minGammaPlus) {
         	whichMin = whichMinus;
         	gamma = minGammaMinus;
@@ -182,13 +182,13 @@ uvec fastLars(const mat& x, const vec& y, const uword& sMax,
         	signs(k) = -1;
         }
         // update correlations
-		r.insert_rows(k, 1, false);			// do not initialize new memory
+		r.insert_rows(k, 1);			// do not initialize new memory
         r(k) = r(k-1) - gamma * a;
 		corY.shed_row(whichMin);
 		corU.shed_row(whichMin);
 		corY = corY - gamma * corU;
 		// update active set
-		active.insert_rows(k, 1, false);	// do not initialize new memory
+		active.insert_rows(k, 1);	// do not initialize new memory
 		active(k) = inactive(whichMin);
 		// update inactive set
 		inactive.shed_row(whichMin);

src/fastLasso.cpp

@@ -286,8 +286,8 @@ void fastLasso(const mat& x, const vec& y, const double& lambda,
   						// correct way
   						// insert row and column without initializing memory
   						// (set_size() and reshape() have strange behavior)
-  						L.insert_rows(k, 1, false);
-  						L.insert_cols(k, 1, false);
+  						L.insert_rows(k, 1);
+  						L.insert_cols(k, 1);
   						// fill new parts of the matrix
   						for(uword j = 0; j < k; j++) {
   							L(k, j) = l(j);
@@ -300,7 +300,7 @@ void fastLasso(const mat& x, const vec& y, const double& lambda,
   				// in case of singularity
   				if(rank == k) {
   					// singularity: drop variable for good
-  					ignores.insert_rows(s, 1, false);	// do not initialize new memory
+  					ignores.insert_rows(s, 1);	// do not initialize new memory
   					ignores(s) = newJ;
   					s++;	// increase number of ignored variables
   					p--;	// decrease number of variables
@@ -310,10 +310,10 @@ void fastLasso(const mat& x, const vec& y, const double& lambda,
   					}
   				} else {
   					// no singularity: add variable to active set
-  					active.insert_rows(k, 1, false);	// do not initialize new memory
+  					active.insert_rows(k, 1);	// do not initialize new memory
   					active(k) = newJ;
   					// keep track of sign of correlation for new active variable
-  					signs.insert_rows(k, 1, false);		// do not initialize new memory
+  					signs.insert_rows(k, 1);		// do not initialize new memory
   					signs(k) = sign(corY(newJ));
   					k++;	// increase number of active variables
   				}
@@ -436,7 +436,7 @@ void fastLasso(const mat& x, const vec& y, const double& lambda,
   			L = symmatu(L);
   			// add dropped variables to inactive set and make sure
   			// coefficients are 0
-  			inactive.insert_rows(m, drops.n_elem, false);
+  			inactive.insert_rows(m, drops.n_elem);
   			for(uword j = 0; j < drops.n_elem; j++) {
   				uword newInactive = active(drops(j));
   				inactive(m + j) = newInactive;
@@ -529,7 +529,7 @@ SEXP R_fastLasso(SEXP R_x, SEXP R_y, SEXP R_lambda, SEXP R_useSubset,
       useGram, false, intercept, coefficients, residuals, crit);
 	if(useIntercept) {
 		// prepend intercept
-		coefficients.insert_rows(0, 1, false);
+		coefficients.insert_rows(0, 1);
 		coefficients(0) = intercept;
 	}
 	return List::create(

src/fastSparseLTS.cpp

@@ -140,7 +140,7 @@ SEXP R_testLasso(SEXP R_x, SEXP R_y, SEXP R_lambda, SEXP R_initial,
 	vec coefficients = subset.coefficients;
 	if(useIntercept) {
 		// prepend intercept
-		coefficients.insert_rows(0, 1, false);
+		coefficients.insert_rows(0, 1);
 		coefficients(0) = subset.intercept;
 	}
 	return List::create(
@@ -196,7 +196,7 @@ SEXP R_testCStep(SEXP R_x, SEXP R_y, SEXP R_lambda, SEXP R_subset,
 	vec coefficients = subset.coefficients;
 	if(useIntercept) {
 		// prepend intercept
-		coefficients.insert_rows(0, 1, false);
+		coefficients.insert_rows(0, 1);
 		coefficients(0) = subset.intercept;
 	}
 	return List::create(
@@ -422,7 +422,7 @@ SEXP R_fastSparseLTS(SEXP R_x, SEXP R_y, SEXP R_lambda, SEXP R_initial,
 	vec coefficients = best.coefficients;
 	if(useIntercept) {
 		// prepend intercept
-		coefficients.insert_rows(0, 1, false);
+		coefficients.insert_rows(0, 1);
 		coefficients(0) = best.intercept;
 	}
 	return List::create(