diff --git a/R/charts.efficient.frontier.R b/R/charts.efficient.frontier.R
index dd8acca..021e692 100644
--- a/R/charts.efficient.frontier.R
+++ b/R/charts.efficient.frontier.R
@@ -842,8 +842,8 @@ chart.EfficientFrontierCompare <- function(R, portfolio, risk_type, n.portfolios
       text(out$frontier[1,m.p], out$frontier[1,2], labels = paste("(", round(out$frontier[1,m.p], 4), ",", round(out$frontier[1,2], 4), ")"), pos = 4, cex = cex.assets)
       text(out$frontier[which(x_diff == x_index),1], out$frontier[which(x_diff == x_index),2], labels = paste("(", round(out$frontier[which(x_diff == x_index),1], 4), ",", round(out$frontier[which(x_diff == x_index),2], 4), ")"), pos = 2, cex = cex.assets)
     }
-    legend.labels <- append(legend.labels, paste("% Change in Risk =", round((out$frontier[1,m.p] - out$frontier[1,1]) * 100 / out$frontier[1,1], 2)))
-    legend.labels <- append(legend.labels, paste("% Change in Return =", round((out$frontier[which(x_diff == x_index),2] - out$frontier[1,2]) * 100 / out$frontier[1,2], 2)))
+    legend.labels <- append(legend.labels, paste("% Decrease in Risk =", round((out$frontier[1,m.p] - out$frontier[1,1]) * 100 / out$frontier[1,1], 2)))
+    legend.labels <- append(legend.labels, paste("% Increase in Return =", round((out$frontier[which(x_diff == x_index),2] - out$frontier[1,2]) * 100 / out$frontier[1,2], 2)))
   }
   legend("bottomright", legend=legend.labels, col=col, lty=lty, lwd=lwd, cex=cex.legend, bty="n")
   return(invisible(out))
diff --git a/R/extract.efficient.frontier.R b/R/extract.efficient.frontier.R
index 18308dc..8955500 100644
--- a/R/extract.efficient.frontier.R
+++ b/R/extract.efficient.frontier.R
@@ -214,7 +214,7 @@ meanvar.efficient.frontier <- function(portfolio, R, optimize_method='ROI', n.po
 #' @return a matrix of objective measure values and weights along the efficient frontier
 #' @author Ross Bennett
 #' @export
-meanetl.efficient.frontier <- function(portfolio, R, optimize_method='ROI', n.portfolios=25, ...){
+meanetl.efficient.frontier <- meanes.efficient.frontier <- function(portfolio, R, optimize_method='ROI', n.portfolios=25, ...){
   if(!is.portfolio(portfolio)) stop("portfolio object must be of class 'portfolio'")
   # step 1: find the minimum return given the constraints
   # step 2: find the maximum return given the constraints
diff --git a/man/optimize.portfolio.Rd b/man/optimize.portfolio.Rd
index 77b10b3..ac8e4f0 100644
--- a/man/optimize.portfolio.Rd
+++ b/man/optimize.portfolio.Rd
@@ -178,8 +178,8 @@ Because these convex optimization problem are standardized, there is no need for
 The \code{multiplier} argument in \code{\link{add.objective}} passed into the complete constraint object are ignored by the ROI solver.
 
 If \code{optimize_method="CVXR"} is specified, a default solver will be selected based on the optimization problem.
-The default solver for Linear Problem and Quadratic Programming will be \code{OSQP}, 
-and the default solver for Second-Order Cone Programming will be \code{SCS}.
+The default solver for Quadratic Programming will be \code{OSQP}, 
+and the default solver for Linear Problem and Second-Order Cone Programming will be \code{SCS}.
 Specified CVXR solver can be given by using \code{optimize_method=c("CVXR", "CVXRsolver")}.
 CVXR supports some commercial solvers, including CBC, CPLEX, GUROBI and MOSEK, and some open source solvers, including GLPK, GLPK_MI, OSQP, SCS and ECOS.
 For example, \code{optimize_method = c("CVXR", "ECOS")} can be specified and the optimization problem will be solved via CVXR using the ECOS solver.