From noreply at r-forge.r-project.org  Fri Jan  3 08:09:59 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Fri,  3 Jan 2014 08:09:59 +0100 (CET)
Subject: [Returnanalytics-commits] r3296 - pkg/PerformanceAnalytics/R
Message-ID: <20140103070959.646311839DE@r-forge.r-project.org>

Author: matthieu_lestel
Date: 2014-01-03 08:09:58 +0100 (Fri, 03 Jan 2014)
New Revision: 3296

Modified:
   pkg/PerformanceAnalytics/R/MSquared.R
Log:
correction of a bug in the computation of MSquared

Modified: pkg/PerformanceAnalytics/R/MSquared.R
===================================================================
--- pkg/PerformanceAnalytics/R/MSquared.R	2013-12-23 06:14:36 UTC (rev 3295)
+++ pkg/PerformanceAnalytics/R/MSquared.R	2014-01-03 07:09:58 UTC (rev 3296)
@@ -23,7 +23,7 @@
 #' @examples
 #'
 #' data(portfolio_bacon)
-#' print(MSquared(portfolio_bacon[,1], portfolio_bacon[,2])) #expected 0.1068
+#' print(MSquared(portfolio_bacon[,1], portfolio_bacon[,2])) #expected 0.10062
 #'
 #' data(managers)
 #' print(MSquared(managers['1996',1], managers['1996',8]))
@@ -49,7 +49,7 @@
         Rp = (prod(1 + Ra))^(Period / length(Ra)) - 1
      	sigp = sqrt(var(Ra)*(length(Ra)-1)/length(Ra))*sqrt(Period)
      	sigm = sqrt(var(Rb)*(length(Rb)-1)/length(Rb))*sqrt(Period)
-        result = (Rp - Rf) * sigp / sigm + Rf
+        result = (Rp - Rf) * sigm / sigp + Rf
      }    
      else {
         result = NA


From noreply at r-forge.r-project.org  Mon Jan  6 15:37:34 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Mon,  6 Jan 2014 15:37:34 +0100 (CET)
Subject: [Returnanalytics-commits] r3297 - pkg/PerformanceAnalytics/R
Message-ID: <20140106143734.EF845186AAA@r-forge.r-project.org>

Author: peter_carl
Date: 2014-01-06 15:37:34 +0100 (Mon, 06 Jan 2014)
New Revision: 3297

Modified:
   pkg/PerformanceAnalytics/R/chart.Correlation.R
Log:
- now passing method into cor.test


Modified: pkg/PerformanceAnalytics/R/chart.Correlation.R
===================================================================
--- pkg/PerformanceAnalytics/R/chart.Correlation.R	2014-01-03 07:09:58 UTC (rev 3296)
+++ pkg/PerformanceAnalytics/R/chart.Correlation.R	2014-01-06 14:37:34 UTC (rev 3297)
@@ -46,7 +46,7 @@
         txt <- paste(prefix, txt, sep="")
         if(missing(cex.cor)) cex <- 0.8/strwidth(txt)
 
-        test <- cor.test(x,y)
+        test <- cor.test(x,y, method=method)
         # borrowed from printCoefmat
         Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
                     cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1),


From noreply at r-forge.r-project.org  Sat Jan 11 00:43:32 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Sat, 11 Jan 2014 00:43:32 +0100 (CET)
Subject: [Returnanalytics-commits] r3298 - pkg/PortfolioAnalytics/R
Message-ID: <20140110234332.B103F1860CF@r-forge.r-project.org>

Author: rossbennett34
Date: 2014-01-11 00:43:31 +0100 (Sat, 11 Jan 2014)
New Revision: 3298

Modified:
   pkg/PortfolioAnalytics/R/optFUN.R
Log:
fixing bug in gmv_opt_toc to get objective value from ROI output.

Modified: pkg/PortfolioAnalytics/R/optFUN.R
===================================================================
--- pkg/PortfolioAnalytics/R/optFUN.R	2014-01-06 14:37:34 UTC (rev 3297)
+++ pkg/PortfolioAnalytics/R/optFUN.R	2014-01-10 23:43:31 UTC (rev 3298)
@@ -759,7 +759,7 @@
   names(weights) <- colnames(R)
   out <- list()
   out$weights <- weights
-  out$out <- roi.result$value
+  out$out <- roi.result$objval
   obj_vals <- list()
   # Calculate the objective values here so that we can use the moments$mean
   # and moments$var that might be passed in by the user.


From noreply at r-forge.r-project.org  Sat Jan 11 22:23:39 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Sat, 11 Jan 2014 22:23:39 +0100 (CET)
Subject: [Returnanalytics-commits] r3299 - in pkg/PortfolioAnalytics: . R
	demo
Message-ID: <20140111212340.0122C186812@r-forge.r-project.org>

Author: rossbennett34
Date: 2014-01-11 22:23:39 +0100 (Sat, 11 Jan 2014)
New Revision: 3299

Added:
   pkg/PortfolioAnalytics/demo/demo_roi_solvers.R
Modified:
   pkg/PortfolioAnalytics/DESCRIPTION
   pkg/PortfolioAnalytics/R/optFUN.R
   pkg/PortfolioAnalytics/R/optimize.portfolio.R
   pkg/PortfolioAnalytics/demo/00Index
Log:
Minor modifications to optFUN and optimize.portfolio for easy way to specify solver for ROI per discussion with Doug.

Modified: pkg/PortfolioAnalytics/DESCRIPTION
===================================================================
--- pkg/PortfolioAnalytics/DESCRIPTION	2014-01-10 23:43:31 UTC (rev 3298)
+++ pkg/PortfolioAnalytics/DESCRIPTION	2014-01-11 21:23:39 UTC (rev 3299)
@@ -24,6 +24,7 @@
     ROI (>= 0.1.0),
     ROI.plugin.glpk (>= 0.0.2),
     ROI.plugin.quadprog (>= 0.0.2),
+    ROI.plugin.symphony (>= 0.0.2),
     pso,
     GenSA,
     corpcor

Modified: pkg/PortfolioAnalytics/R/optFUN.R
===================================================================
--- pkg/PortfolioAnalytics/R/optFUN.R	2014-01-10 23:43:31 UTC (rev 3298)
+++ pkg/PortfolioAnalytics/R/optFUN.R	2014-01-11 21:23:39 UTC (rev 3299)
@@ -1,8 +1,9 @@
 
 ##### GMV and QU QP Function #####
-#' Optimization function to solve minimum variance or maximum quadratic utility problems
+#' GMV/QU QP Optimization
 #' 
-#' This function is called by optimize.portfolio to solve minimum variance or maximum quadratic utility problems
+#' This function is called by optimize.portfolio to solve minimum variance or 
+#' maximum quadratic utility problems
 #' 
 #' @param R xts object of asset returns
 #' @param constraints object of constraints in the portfolio object extracted with \code{get_constraints}
@@ -10,9 +11,10 @@
 #' @param lambda risk_aversion parameter
 #' @param target target return value
 #' @param lambda_hhi concentration aversion parameter
-#' @param conc_groups list of vectors specifying the groups of the assets. 
+#' @param conc_groups list of vectors specifying the groups of the assets.
+#' @param solver solver to use
 #' @author Ross Bennett
-gmv_opt <- function(R, constraints, moments, lambda, target, lambda_hhi, conc_groups){
+gmv_opt <- function(R, constraints, moments, lambda, target, lambda_hhi, conc_groups, solver="quadprog"){
   stopifnot("package:ROI" %in% search() || require("ROI", quietly = TRUE))
   stopifnot("package:ROI.plugin.quadprog" %in% search() || require("ROI.plugin.quadprog", quietly = TRUE))
   
@@ -119,7 +121,7 @@
   # set up the optimization problem and solve
   opt.prob <- OP(objective=ROI_objective, 
                        constraints=L_constraint(L=Amat, dir=dir.vec, rhs=rhs.vec))
-  result <- ROI_solve(x=opt.prob, solver="quadprog")
+  result <- ROI_solve(x=opt.prob, solver=solver)
   
   # result <- try(solve.QP(Dmat=Dmat, dvec=dvec, Amat=t(Amat), bvec=rhs.vec, meq=meq), silent=TRUE)
   if(inherits(x=result, "try-error")) stop(paste("No solution found:", result))
@@ -152,16 +154,17 @@
 }
 
 ##### Maximize Return LP Function #####
-#' Optimization function to solve minimum variance or maximum quadratic utility problems
+#' Maximum Return LP Optimization
 #' 
-#' This function is called by optimize.portfolio to solve minimum variance or maximum quadratic utility problems
+#' This function is called by optimize.portfolio to solve maximum return
 #' 
 #' @param R xts object of asset returns
 #' @param constraints object of constraints in the portfolio object extracted with \code{get_constraints}
 #' @param moments object of moments computed based on objective functions
 #' @param target target return value
+#' @param solver solver to use
 #' @author Ross Bennett
-maxret_opt <- function(R, moments, constraints, target){
+maxret_opt <- function(R, moments, constraints, target, solver="glpk"){
   stopifnot("package:ROI" %in% search() || require("ROI",quietly = TRUE))
   stopifnot("package:ROI.plugin.glpk" %in% search() || require("ROI.plugin.glpk",quietly = TRUE))
   
@@ -224,7 +227,7 @@
   opt.prob <- OP(objective=ROI_objective, 
                  constraints=L_constraint(L=Amat, dir=dir.vec, rhs=rhs.vec),
                  bounds=bnds)
-  roi.result <- ROI_solve(x=opt.prob, solver="glpk")
+  roi.result <- ROI_solve(x=opt.prob, solver=solver)
   
   # roi.result <- Rglpk_solve_LP(obj=objL, mat=Amat, dir=dir.vec, rhs=rhs.vec, bounds=bnds)
   
@@ -255,16 +258,18 @@
 }
 
 ##### Maximize Return MILP Function #####
-#' Optimization function to solve maximum return problems
+#' Maximum Return MILP Optimization
 #' 
-#' This function is called by optimize.portfolio to solve maximum return problems via mixed integer linear programming.
+#' This function is called by optimize.portfolio to solve maximum return 
+#' problems via mixed integer linear programming.
 #' 
 #' @param R xts object of asset returns
 #' @param constraints object of constraints in the portfolio object extracted with \code{get_constraints}
 #' @param moments object of moments computed based on objective functions
 #' @param target target return value
+#' @param solver solver to use
 #' @author Ross Bennett
-maxret_milp_opt <- function(R, constraints, moments, target){
+maxret_milp_opt <- function(R, constraints, moments, target, solver="glpk"){
   stopifnot("package:ROI" %in% search() || require("ROI",quietly = TRUE))
   stopifnot("package:ROI.plugin.glpk" %in% search() || require("ROI.plugin.glpk",quietly = TRUE))
   
@@ -356,7 +361,7 @@
   opt.prob <- OP(objective=ROI_objective, 
                  constraints=L_constraint(L=Amat, dir=dir, rhs=rhs),
                  bounds=bnds, types=types)
-  roi.result <- try(ROI_solve(x=opt.prob, solver="glpk"), silent=TRUE)
+  roi.result <- try(ROI_solve(x=opt.prob, solver=solver), silent=TRUE)
   if(inherits(roi.result, "try-error")) stop(paste("No solution found:", roi.result))
   
   # Weights
@@ -377,7 +382,7 @@
 }
 
 ##### Minimize ETL LP Function #####
-#' Optimization function to solve minimum ETL problems
+#' Minimum ETL LP Optimization
 #' 
 #' This function is called by optimize.portfolio to solve minimum ETL problems.
 #' 
@@ -386,8 +391,9 @@
 #' @param moments object of moments computed based on objective functions
 #' @param target target return value
 #' @param alpha alpha value for ETL/ES/CVaR
+#' @param solver solver to use
 #' @author Ross Bennett
-etl_opt <- function(R, constraints, moments, target, alpha){
+etl_opt <- function(R, constraints, moments, target, alpha, solver="glpk"){
   stopifnot("package:ROI" %in% search() || require("ROI",quietly = TRUE))
   stopifnot("package:ROI.plugin.glpk" %in% search() || require("ROI.plugin.glpk",quietly = TRUE))
   
@@ -442,7 +448,7 @@
   opt.prob <- OP(objective=ROI_objective, 
                        constraints=L_constraint(L=Amat, dir=dir.vec, rhs=rhs.vec),
                        bounds=bnds)
-  roi.result <- try(ROI_solve(x=opt.prob, solver="glpk"), silent=TRUE)
+  roi.result <- try(ROI_solve(x=opt.prob, solver=solver), silent=TRUE)
   if(inherits(x=roi.result, "try-error")) stop(paste("No solution found:", roi.result))
   
   weights <- roi.result$solution[1:N]
@@ -473,17 +479,19 @@
 }
 
 ##### Minimize ETL MILP Function #####
-#' Optimization function to solve minimum ETL problems
+#' Minimum ETL MILP Optimization
 #' 
-#' This function is called by optimize.portfolio to solve minimum ETL problems via mixed integer linear programming.
+#' This function is called by optimize.portfolio to solve minimum ETL problems 
+#' via mixed integer linear programming.
 #' 
 #' @param R xts object of asset returns
 #' @param constraints object of constraints in the portfolio object extracted with \code{get_constraints}
 #' @param moments object of moments computed based on objective functions
 #' @param target target return value
 #' @param alpha alpha value for ETL/ES/CVaR
+#' @param solver solver to use
 #' @author Ross Bennett
-etl_milp_opt <- function(R, constraints, moments, target, alpha){
+etl_milp_opt <- function(R, constraints, moments, target, alpha, solver="glpk"){
   stopifnot("package:ROI" %in% search() || require("ROI",quietly = TRUE))
   stopifnot("package:ROI.plugin.glpk" %in% search() || require("ROI.plugin.glpk",quietly = TRUE))
   
@@ -589,7 +597,7 @@
   opt.prob <- OP(objective=ROI_objective, 
                  constraints=L_constraint(L=tmpAmat, dir=dir, rhs=rhs),
                  bounds=bnds, types=types)
-  roi.result <- ROI_solve(x=opt.prob, solver="glpk")
+  roi.result <- ROI_solve(x=opt.prob, solver=solver)
   
   # The Rglpk solvers status returns an an integer with status information
   # about the solution returned: 0 if the optimal solution was found, a 
@@ -626,9 +634,10 @@
 }
 
 ##### minimize variance or maximize quadratic utility with turnover constraints #####
-#' Optimization function to solve minimum variance or maximum quadratic utility problems with turnover constraint
+#' GMV/QU QP Optimization with Turnover Constraint
 #' 
-#' This function is called by optimize.portfolio to solve minimum variance or maximum quadratic utility problems
+#' This function is called by optimize.portfolio to solve minimum variance or 
+#' maximum quadratic utility problems with turnover constraint
 #' 
 #' @param R xts object of asset returns
 #' @param constraints object of constraints in the portfolio object extracted with \code{get_constraints}
@@ -636,8 +645,9 @@
 #' @param lambda risk_aversion parameter
 #' @param target target return value
 #' @param init_weights initial weights to compute turnover
+#' @param solver solver to use
 #' @author Ross Bennett
-gmv_opt_toc <- function(R, constraints, moments, lambda, target, init_weights){
+gmv_opt_toc <- function(R, constraints, moments, lambda, target, init_weights, solver="quadprog"){
   # function for minimum variance or max quadratic utility problems
   stopifnot("package:corpcor" %in% search() || require("corpcor",quietly = TRUE))
   stopifnot("package:ROI" %in% search() || require("ROI", quietly = TRUE))
@@ -748,7 +758,7 @@
   opt.prob <- OP(objective=ROI_objective, 
                  constraints=L_constraint(L=Amat, dir=dir, rhs=rhs))
   
-  roi.result <- try(ROI_solve(x=opt.prob, solver="quadprog"), silent=TRUE)
+  roi.result <- try(ROI_solve(x=opt.prob, solver=solver), silent=TRUE)
   
   if(inherits(roi.result, "try-error")) stop(paste("No solution found:", roi.result))
   
@@ -779,8 +789,21 @@
   return(out)
 }
 
-# proportional transaction cost constraint
-gmv_opt_ptc <- function(R, constraints, moments, lambda, target, init_weights){
+##### minimize variance or maximize quadratic utility with proportional transactioncosts constraints #####
+#' GMV/QU QP Optimization with Proportional Transaction Cost Constraint
+#' 
+#' This function is called by optimize.portfolio to solve minimum variance or 
+#' maximum quadratic utility problems with proportional transaction cost constraint
+#' 
+#' @param R xts object of asset returns
+#' @param constraints object of constraints in the portfolio object extracted with \code{get_constraints}
+#' @param moments object of moments computed based on objective functions
+#' @param lambda risk_aversion parameter
+#' @param target target return value
+#' @param init_weights initial weights to compute turnover
+#' @param solver solver to use
+#' @author Ross Bennett
+gmv_opt_ptc <- function(R, constraints, moments, lambda, target, init_weights, solver="quadprog"){
   # function for minimum variance or max quadratic utility problems
   # modifying ProportionalCostOpt function from MPO package
   stopifnot("package:corpcor" %in% search() || require("corpcor", quietly = TRUE))
@@ -880,7 +903,7 @@
   
   opt.prob <- OP(objective=ROI_objective, 
                  constraints=L_constraint(L=Amat, dir=dir, rhs=rhs))
-  roi.result <- try(ROI_solve(x=opt.prob, solver="quadprog"), silent=TRUE)
+  roi.result <- try(ROI_solve(x=opt.prob, solver=solver), silent=TRUE)
 
   if(inherits(roi.result, "try-error")) stop(paste("No solution found:", roi.result))
   
@@ -915,7 +938,7 @@
 }
 
 
-mean_etl_opt <- function(R, constraints, moments, target, alpha, tol=.Machine$double.eps^0.5, maxit=50){
+mean_etl_opt <- function(R, constraints, moments, target, alpha, solver="glpk", tol=.Machine$double.eps^0.5, maxit=50){
   # This function returns the target mean return that maximizes mean / etl (i.e. starr)
   
   # if all(moments$mean == 0) then the user did not specify mean as an objective,
@@ -929,17 +952,17 @@
   
   # Find the maximum return
   if(!is.null(constraints$max_pos)){
-    max_ret <- maxret_milp_opt(R=R, constraints=constraints, moments=moments, target=NA)
+    max_ret <- maxret_milp_opt(R=R, constraints=constraints, moments=moments, target=NA, solver=solver)
   } else {
-    max_ret <- maxret_opt(R=R, moments=moments, constraints=constraints, target=NA)
+    max_ret <- maxret_opt(R=R, moments=moments, constraints=constraints, target=NA, solver=solver)
   }
   max_mean <- as.numeric(-max_ret$out)
   
   # Find the starr at the maximum etl portfolio
   if(!is.null(constraints$max_pos)){
-    ub_etl <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=max_mean, alpha=alpha)
+    ub_etl <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=max_mean, alpha=alpha, solver=solver)
   } else {
-    ub_etl <- etl_opt(R=R, constraints=constraints, moments=moments, target=max_mean, alpha=alpha)
+    ub_etl <- etl_opt(R=R, constraints=constraints, moments=moments, target=max_mean, alpha=alpha, solver=solver)
   }
   ub_weights <- matrix(ub_etl$weights, ncol=1)
   ub_mean <- as.numeric(t(ub_weights) %*% fmean)
@@ -954,9 +977,9 @@
   
   # Find the starr at the minimum etl portfolio
   if(!is.null(constraints$max_pos)){
-    lb_etl <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=NA, alpha=alpha)
+    lb_etl <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=NA, alpha=alpha, solver=solver)
   } else {
-    lb_etl <- etl_opt(R=R, constraints=constraints, moments=moments, target=NA, alpha=alpha)
+    lb_etl <- etl_opt(R=R, constraints=constraints, moments=moments, target=NA, alpha=alpha, solver=solver)
   }
   lb_weights <- matrix(lb_etl$weights)  
   lb_mean <- as.numeric(t(lb_weights) %*% fmean)  
@@ -986,9 +1009,9 @@
     # Find the starr at the mean return midpoint
     new_ret <- (lb_mean + ub_mean) / 2
     if(!is.null(constraints$max_pos)){
-      mid <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha)
+      mid <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha, solver=solver)
     } else {
-      mid <- etl_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha)
+      mid <- etl_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha, solver=solver)
     }
     # print(mid)
     mid_weights <- matrix(mid$weights, ncol=1)
@@ -1009,9 +1032,9 @@
       ub_starr <- mid_starr
       new_ret <- (lb_mean + ub_mean) / 2
       if(!is.null(constraints$max_pos)){
-        mid <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha)
+        mid <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha, solver=solver)
       } else {
-        mid <- etl_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha)
+        mid <- etl_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha, solver=solver)
       }
       mid_weights <- matrix(mid$weights, ncol=1)
       mid_mean <- as.numeric(t(mid_weights) %*% fmean)
@@ -1025,9 +1048,9 @@
       lb_starr <- mid_starr
       new_ret <- (lb_mean + ub_mean) / 2
       if(!is.null(constraints$max_pos)){
-        mid <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha)
+        mid <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha, solver=solver)
       } else {
-        mid <- etl_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha)
+        mid <- etl_opt(R=R, constraints=constraints, moments=moments, target=new_ret, alpha=alpha, solver=solver)
       }
       mid_weights <- matrix(mid$weights, ncol=1)
       mid_mean <- as.numeric(t(mid_weights) %*% fmean)
@@ -1041,7 +1064,7 @@
   return(new_ret)
 }
 
-max_sr_opt <- function(R, constraints, moments, lambda, target, lambda_hhi, conc_groups, tol=.Machine$double.eps^0.5, maxit=50){
+max_sr_opt <- function(R, constraints, moments, lambda, target, lambda_hhi, conc_groups, solver="quadprog", tol=.Machine$double.eps^0.5, maxit=50){
   # This function returns the target mean return that maximizes mean / sd (i.e. sharpe ratio)
   
   # get the forecast mean from moments
@@ -1061,7 +1084,7 @@
   # Calculate the sr at the miminum var portfolio
   tmpmoments <- moments
   tmpmoments$mean <- rep(0, length(moments$mean))
-  lb_sr <- gmv_opt(R=R, constraints=constraints, moments=tmpmoments, lambda=1, target=NA, lambda_hhi=lambda_hhi, conc_groups=conc_groups)
+  lb_sr <- gmv_opt(R=R, constraints=constraints, moments=tmpmoments, lambda=1, target=NA, lambda_hhi=lambda_hhi, conc_groups=conc_groups, solver=solver)
   lb_weights <- matrix(lb_sr$weights)
   lb_mean <- as.numeric(t(lb_weights) %*% fmean)
   lb_sd <- as.numeric(sqrt(t(lb_weights) %*% moments$var %*% lb_weights))
@@ -1079,7 +1102,7 @@
     
     # Find the starr at the mean return midpoint
     new_ret <- (lb_mean + ub_mean) / 2
-    mid <- gmv_opt(R=R, constraints=constraints, moments=tmpmoments, lambda=1, target=new_ret, lambda_hhi=lambda_hhi, conc_groups=conc_groups)
+    mid <- gmv_opt(R=R, constraints=constraints, moments=tmpmoments, lambda=1, target=new_ret, lambda_hhi=lambda_hhi, conc_groups=conc_groups, solver=solver)
     mid_weights <- matrix(mid$weights, ncol=1)
     mid_mean <- as.numeric(t(mid_weights) %*% fmean)
     mid_sd <- as.numeric(sqrt(t(mid_weights) %*% moments$var %*% mid_weights))
@@ -1096,7 +1119,7 @@
       ub_mean <- mid_mean
       ub_sr <- mid_sr
       new_ret <- (lb_mean + ub_mean) / 2
-      mid <- gmv_opt(R=R, constraints=constraints, moments=tmpmoments, lambda=1, target=new_ret, lambda_hhi=lambda_hhi, conc_groups=conc_groups)
+      mid <- gmv_opt(R=R, constraints=constraints, moments=tmpmoments, lambda=1, target=new_ret, lambda_hhi=lambda_hhi, conc_groups=conc_groups, solver=solver)
       mid_weights <- matrix(mid$weights, ncol=1)
       mid_mean <- as.numeric(t(mid_weights) %*% fmean)
       mid_sd <- as.numeric(sqrt(t(mid_weights) %*% moments$var %*% mid_weights))
@@ -1106,7 +1129,7 @@
       lb_mean <- mid_mean
       lb_sr <- mid_sr
       new_ret <- (lb_mean + ub_mean) / 2
-      mid <- gmv_opt(R=R, constraints=constraints, moments=tmpmoments, lambda=1, target=new_ret, lambda_hhi=lambda_hhi, conc_groups=conc_groups)
+      mid <- gmv_opt(R=R, constraints=constraints, moments=tmpmoments, lambda=1, target=new_ret, lambda_hhi=lambda_hhi, conc_groups=conc_groups, solver=solver)
       mid_weights <- matrix(mid$weights, ncol=1)
       mid_mean <- as.numeric(t(mid_weights) %*% fmean)
       mid_sd <- as.numeric(sqrt(t(mid_weights) %*% moments$var %*% mid_weights))

Modified: pkg/PortfolioAnalytics/R/optimize.portfolio.R
===================================================================
--- pkg/PortfolioAnalytics/R/optimize.portfolio.R	2014-01-10 23:43:31 UTC (rev 3298)
+++ pkg/PortfolioAnalytics/R/optimize.portfolio.R	2014-01-11 21:23:39 UTC (rev 3299)
@@ -438,7 +438,7 @@
   portfolio=NULL,
   constraints=NULL,
   objectives=NULL,
-  optimize_method=c("DEoptim","random","ROI","ROI_old","pso","GenSA"),
+  optimize_method=c("DEoptim","random","ROI","pso","GenSA"),
   search_size=20000,
   trace=FALSE, ...,
   rp=NULL,
@@ -725,11 +725,11 @@
     
   } ## end case for random
   
-  if(optimize_method == "ROI"){
-    # This takes in a regular portfolio object and extracts the desired business objectives
-    # and converts them to matrix form to be inputed into a closed form solver
-    # retrieve the objectives to minimize, these should either be "var" and/or "mean"
-    # we can either miniminze variance or maximize quiadratic utility (we will be minimizing the neg. quad. utility)
+  roi_solvers <- c("ROI", "quadprog", "glpk", "symphony", "ipop", "cplex")
+  if(optimize_method %in% roi_solvers){
+    # This takes in a regular portfolio object and extracts the constraints and
+    #  objectives and converts them for input. to a closed form solver using
+    # ROI as an interface.
     moments <- list(mean=rep(0, N))
     alpha <- 0.05
     if(!is.null(constraints$return_target)){
@@ -737,13 +737,6 @@
     } else {
       target <- NA
     }
-    # comment out so concentration aversion can only be specified as an objective
-    # because it is added to the quadratic objective term for QP problems (minvar and qu)
-    # if(!is.null(constraints$conc_aversion)){
-    #  lambda_hhi <- constraints$conc_aversion
-    #} else {
-    #  lambda_hhi <- 0
-    #}
     lambda <- 1
     
     # list of valid objective names for ROI solvers
@@ -802,6 +795,13 @@
     }
     
     if("var" %in% names(moments)){
+      # Set a default solver if optimize_method == "ROI", otherwise assume the
+      # optimize_method specified by the user is the solver for ROI
+      if(optimize_method == "ROI"){
+        solver <- "quadprog"
+      } else {
+        solver <- optimize_method
+      }
       # Minimize variance if the only objective specified is variance
       # Maximize Quadratic Utility if var and mean are specified as objectives
       if(!is.null(constraints$turnover_target) | !is.null(constraints$ptc)){
@@ -810,14 +810,14 @@
           constraints$ptc <- NULL
         }
         if(!is.null(constraints$turnover_target) & is.null(constraints$ptc)){
-          qp_result <- gmv_opt_toc(R=R, constraints=constraints, moments=moments, lambda=lambda, target=target, init_weights=portfolio$assets)
+          qp_result <- gmv_opt_toc(R=R, constraints=constraints, moments=moments, lambda=lambda, target=target, init_weights=portfolio$assets, solver=solver)
           weights <- qp_result$weights
           # obj_vals <- constrained_objective(w=weights, R=R, portfolio, trace=TRUE, normalize=FALSE)$objective_measures
           obj_vals <- qp_result$obj_vals
           out <- list(weights=weights, objective_measures=obj_vals, opt_values=obj_vals, out=qp_result$out, call=call)
         }
         if(!is.null(constraints$ptc) & is.null(constraints$turnover_target)){
-          qp_result <- gmv_opt_ptc(R=R, constraints=constraints, moments=moments, lambda=lambda, target=target, init_weights=portfolio$assets)
+          qp_result <- gmv_opt_ptc(R=R, constraints=constraints, moments=moments, lambda=lambda, target=target, init_weights=portfolio$assets, solver=solver)
           weights <- qp_result$weights
           # obj_vals <- constrained_objective(w=weights, R=R, portfolio, trace=TRUE, normalize=FALSE)$objective_measures
           obj_vals <- qp_result$obj_vals
@@ -827,12 +827,12 @@
         # if(hasArg(ef)) ef=match.call(expand.dots=TRUE)$ef else ef=FALSE
         if(hasArg(maxSR)) maxSR=match.call(expand.dots=TRUE)$maxSR else maxSR=FALSE
         if(maxSR){
-          target <- max_sr_opt(R=R, constraints=constraints, moments=moments, lambda=lambda, target=target, lambda_hhi=lambda_hhi, conc_groups=conc_groups)
+          target <- max_sr_opt(R=R, constraints=constraints, moments=moments, lambda=lambda, target=target, lambda_hhi=lambda_hhi, conc_groups=conc_groups, solver=solver)
           # need to set moments$mean=0 here because quadratic utility and target return is sensitive to returning no solution
           tmp_moments_mean <- moments$mean
           moments$mean <- rep(0, length(moments$mean))
         }
-        roi_result <- gmv_opt(R=R, constraints=constraints, moments=moments, lambda=lambda, target=target, lambda_hhi=lambda_hhi, conc_groups=conc_groups)
+        roi_result <- gmv_opt(R=R, constraints=constraints, moments=moments, lambda=lambda, target=target, lambda_hhi=lambda_hhi, conc_groups=conc_groups, solver=solver)
         weights <- roi_result$weights
         # obj_vals <- constrained_objective(w=weights, R=R, portfolio, trace=TRUE, normalize=FALSE)$objective_measures
         obj_vals <- roi_result$obj_vals
@@ -846,17 +846,25 @@
       }
     }
     if(length(names(moments)) == 1 & "mean" %in% names(moments)) {
+      # Set a default solver if optimize_method == "ROI", otherwise assume the
+      # optimize_method specified by the user is the solver for ROI
+      if(optimize_method == "ROI"){
+        solver <- "glpk"
+      } else {
+        solver <- optimize_method
+      }
+      
       # Maximize return if the only objective specified is mean
       if(!is.null(constraints$max_pos) | !is.null(constraints$leverage)) {
         # This is an MILP problem if max_pos is specified as a constraint
-        roi_result <- maxret_milp_opt(R=R, constraints=constraints, moments=moments, target=target)
+        roi_result <- maxret_milp_opt(R=R, constraints=constraints, moments=moments, target=target, solver=solver)
         weights <- roi_result$weights
         # obj_vals <- constrained_objective(w=weights, R=R, portfolio, trace=TRUE, normalize=FALSE)$objective_measures
         obj_vals <- roi_result$obj_vals
         out <- list(weights=weights, objective_measures=obj_vals, opt_values=obj_vals, out=roi_result$out, call=call)
       } else {
         # Maximize return LP problem
-        roi_result <- maxret_opt(R=R, constraints=constraints, moments=moments, target=target)
+        roi_result <- maxret_opt(R=R, constraints=constraints, moments=moments, target=target, solver=solver)
         weights <- roi_result$weights
         # obj_vals <- constrained_objective(w=weights, R=R, portfolio, trace=TRUE, normalize=FALSE)$objective_measures
         obj_vals <- roi_result$obj_vals
@@ -864,6 +872,14 @@
       }
     }
     if( any(c("CVaR", "ES", "ETL") %in% names(moments)) ) {
+      # Set a default solver if optimize_method == "ROI", otherwise assume the
+      # optimize_method specified by the user is the solver for ROI
+      if(optimize_method == "ROI"){
+        solver <- "glpk"
+      } else {
+        solver <- optimize_method
+      }
+      
       if(hasArg(ef)) ef=match.call(expand.dots=TRUE)$ef else ef=FALSE
       if(hasArg(maxSTARR)) maxSTARR=match.call(expand.dots=TRUE)$maxSTARR else maxSTARR=TRUE
       if(ef) meanetl <- TRUE else meanetl <- FALSE
@@ -872,12 +888,12 @@
       # Minimize sample ETL/ES/CVaR if CVaR, ETL, or ES is specified as an objective
       if(length(moments) == 2 & all(moments$mean != 0) & ef==FALSE & maxSTARR){
         # This is called by meanetl.efficient.frontier and we do not want that for efficient frontiers, need to have ef==FALSE
-        target <- mean_etl_opt(R=R, constraints=constraints, moments=moments, target=target, alpha=alpha)
+        target <- mean_etl_opt(R=R, constraints=constraints, moments=moments, target=target, alpha=alpha, solver=solver)
         meanetl <- TRUE
       }
       if(!is.null(constraints$max_pos)) {
         # This is an MILP problem if max_pos is specified as a constraint
-        roi_result <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=target, alpha=alpha)
+        roi_result <- etl_milp_opt(R=R, constraints=constraints, moments=moments, target=target, alpha=alpha, solver=solver)
         weights <- roi_result$weights
         # obj_vals <- constrained_objective(w=weights, R=R, portfolio, trace=TRUE, normalize=FALSE)$objective_measures
         # obj_vals <- roi_result$obj_vals
@@ -888,7 +904,7 @@
         out <- list(weights=weights, objective_measures=obj_vals, opt_values=obj_vals, out=roi_result$out, call=call)
       } else {
         # Minimize sample ETL/ES/CVaR LP Problem
-        roi_result <- etl_opt(R=R, constraints=constraints, moments=moments, target=target, alpha=alpha)
+        roi_result <- etl_opt(R=R, constraints=constraints, moments=moments, target=target, alpha=alpha, solver=solver)
         weights <- roi_result$weights
         # obj_vals <- constrained_objective(w=weights, R=R, portfolio, trace=TRUE, normalize=FALSE)$objective_measures
         # obj_vals <- roi_result$obj_vals
@@ -898,6 +914,8 @@
         out <- list(weights=weights, objective_measures=obj_vals, opt_values=obj_vals, out=roi_result$out, call=call)
       }
     }
+    # Set here at the end so we get optimize.portfolio.ROI and not optimize.portfolio.{solver} classes
+    optimize_method <- "ROI"
   } ## end case for ROI
   
   ## case if method=pso---particle swarm
@@ -1032,6 +1050,13 @@
 #' 
 #' When using GenSA and want to set \code{verbose=TRUE}, instead use \code{trace}. 
 #' 
+#' If \code{optimize_method="ROI"} is specified, a default solver will be 
+#' selected based on the optimization problem. The \code{glpk} solver is the
+#' default solver for LP and MILP optimization problems. The \code{quadprog} 
+#' solver is the default solver for QP optimization problems. For example,
+#' \code{optimize_method = "quadprog"} can be specified and the optimization
+#' problem will be solved via ROI using the quadprog solver.
+#' 
 #' The extension to ROI solves a limited type of convex optimization problems:
 #' \itemize{
 #' \item{Maxmimize portfolio return subject leverage, box, group, position limit, target mean return, and/or factor exposure constraints on weights.}
@@ -1063,7 +1088,8 @@
 #' @param portfolio an object of type "portfolio" specifying the constraints and objectives for the optimization
 #' @param constraints default=NULL, a list of constraint objects. An object of class v1_constraint' can be passed in here.
 #' @param objectives default=NULL, a list of objective objects.
-#' @param optimize_method one of "DEoptim", "random", "ROI","ROI_old", "pso", "GenSA".  For using \code{ROI_old}, need to use a constraint_ROI object in constraints. For using \code{ROI}, pass standard \code{constratint} object in \code{constraints} argument.  Presently, ROI has plugins for \code{quadprog} and \code{Rglpk}.
+#' @param optimize_method one of "DEoptim", "random", "ROI", "pso", "GenSA". A solver
+#' for ROI can also be specified and will be solved using ROI. See Details.
 #' @param search_size integer, how many portfolios to test, default 20,000
 #' @param trace TRUE/FALSE if TRUE will attempt to return additional information on the path or portfolios searched
 #' @param \dots any other passthru parameters

Modified: pkg/PortfolioAnalytics/demo/00Index
===================================================================
--- pkg/PortfolioAnalytics/demo/00Index	2014-01-10 23:43:31 UTC (rev 3298)
+++ pkg/PortfolioAnalytics/demo/00Index	2014-01-11 21:23:39 UTC (rev 3299)
@@ -24,4 +24,5 @@
 demo_min_StdDev Demonstrate objective to minimize portfolio standard deviation.
 demo_min_expected_shortfall Demonstrate objective to minimize expected shortfall.
 demo_risk_budgets Demonstrate using risk budget objectives.
+demo_roi_solvers Demonstrate specifying a solver using ROI
 

Added: pkg/PortfolioAnalytics/demo/demo_roi_solvers.R
===================================================================
--- pkg/PortfolioAnalytics/demo/demo_roi_solvers.R	                        (rev 0)
+++ pkg/PortfolioAnalytics/demo/demo_roi_solvers.R	2014-01-11 21:23:39 UTC (rev 3299)
@@ -0,0 +1,53 @@
+
+library(PortfolioAnalytics)
+library(ROI)
+library(ROI.plugin.quadprog)
+library(ROI.plugin.glpk)
+library(ROI.plugin.symphony)
+
+data(edhec)
+R <- edhec[, 1:4]
+funds <- colnames(R)
+
+# Set up initial portfolio with basic constraints
+init.portf <- portfolio.spec(assets=funds)
+init.portf <- add.constraint(portfolio=init.portf, type="full_investment")
+init.portf <- add.constraint(portfolio=init.portf, type="long_only")
+
+# Add objectives
+maxret.portf <- add.objective(portfolio=init.portf, type="return", name="mean")
+mines.portf <- add.objective(portfolio=init.portf, type="risk", name="ES")
+minsd.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev")
+qu.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev", 
+                          risk_aversion=0.25)
+qu.portf <- add.objective(portfolio=qu.portf, type="return", name="mean")
+
+# Maximize return
+opt.maxret.roi <- optimize.portfolio(R, maxret.portf, optimize_method="ROI")
+opt.maxret.glpk <- optimize.portfolio(R, maxret.portf, optimize_method="glpk")
+opt.maxret.symphony <- optimize.portfolio(R, maxret.portf, optimize_method="symphony")
+all.equal(extractStats(opt.maxret.roi), extractStats(opt.maxret.glpk))
+all.equal(extractStats(opt.maxret.roi), extractStats(opt.maxret.symphony))
+# This fails because an optimization problem with a linear objective cannot
+# be solved with a quadratic programming solver
+# opt.maxret.qp <- optimize.portfolio(R, maxret.portf, optimize_method="quadprog")
+
+# Minimize ES
+opt.mines.roi <- optimize.portfolio(R, mines.portf, optimize_method="ROI")
+opt.mines.glpk <- optimize.portfolio(R, mines.portf, optimize_method="glpk")
+opt.mines.symphony <- optimize.portfolio(R, mines.portf, optimize_method="symphony")
+all.equal(extractStats(opt.mines.roi), extractStats(opt.mines.glpk))
+all.equal(extractStats(opt.mines.roi), extractStats(opt.mines.symphony))
+
+# Minimize standard deviation
+opt.minsd.roi <- optimize.portfolio(R, minsd.portf, optimize_method="ROI")
+opt.minsd.qp <- optimize.portfolio(R, minsd.portf, optimize_method="quadprog")
+all.equal(extractStats(opt.minsd.roi), extractStats(opt.minsd.qp))
+# This fails because an optimization problem with a quadratic objective cannot
+# be solved with a linear programming solver
+# opt.minsd.glpk <- optimize.portfolio(R, minsd.portf, optimize_method="glpk")
+
+# Maximize quadratic utility
[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/returnanalytics -r 3299

From noreply at r-forge.r-project.org  Sat Jan 11 22:26:56 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Sat, 11 Jan 2014 22:26:56 +0100 (CET)
Subject: [Returnanalytics-commits] r3300 - pkg/PortfolioAnalytics/man
Message-ID: <20140111212656.8B54718699C@r-forge.r-project.org>

Author: rossbennett34
Date: 2014-01-11 22:26:56 +0100 (Sat, 11 Jan 2014)
New Revision: 3300

Added:
   pkg/PortfolioAnalytics/man/gmv_opt_ptc.Rd
Modified:
   pkg/PortfolioAnalytics/man/etl_milp_opt.Rd
   pkg/PortfolioAnalytics/man/etl_opt.Rd
   pkg/PortfolioAnalytics/man/gmv_opt.Rd
   pkg/PortfolioAnalytics/man/gmv_opt_toc.Rd
   pkg/PortfolioAnalytics/man/maxret_milp_opt.Rd
   pkg/PortfolioAnalytics/man/maxret_opt.Rd
   pkg/PortfolioAnalytics/man/optimize.portfolio.Rd
Log:
Updating documentation for optimization sub-functions and optimize.portfolio

Modified: pkg/PortfolioAnalytics/man/etl_milp_opt.Rd
===================================================================
--- pkg/PortfolioAnalytics/man/etl_milp_opt.Rd	2014-01-11 21:23:39 UTC (rev 3299)
+++ pkg/PortfolioAnalytics/man/etl_milp_opt.Rd	2014-01-11 21:26:56 UTC (rev 3300)
@@ -1,8 +1,9 @@
 \name{etl_milp_opt}
 \alias{etl_milp_opt}
-\title{Optimization function to solve minimum ETL problems}
+\title{Minimum ETL MILP Optimization}
 \usage{
-  etl_milp_opt(R, constraints, moments, target, alpha)
+  etl_milp_opt(R, constraints, moments, target, alpha,
+    solver = "glpk")
 }
 \arguments{
   \item{R}{xts object of asset returns}
@@ -16,6 +17,8 @@
   \item{target}{target return value}
 
   \item{alpha}{alpha value for ETL/ES/CVaR}
+
+  \item{solver}{solver to use}
 }
 \description{
   This function is called by optimize.portfolio to solve

Modified: pkg/PortfolioAnalytics/man/etl_opt.Rd
===================================================================
--- pkg/PortfolioAnalytics/man/etl_opt.Rd	2014-01-11 21:23:39 UTC (rev 3299)
+++ pkg/PortfolioAnalytics/man/etl_opt.Rd	2014-01-11 21:26:56 UTC (rev 3300)
@@ -1,8 +1,9 @@
 \name{etl_opt}
 \alias{etl_opt}
-\title{Optimization function to solve minimum ETL problems}
+\title{Minimum ETL LP Optimization}
 \usage{
-  etl_opt(R, constraints, moments, target, alpha)
+  etl_opt(R, constraints, moments, target, alpha,
+    solver = "glpk")
 }
 \arguments{
   \item{R}{xts object of asset returns}
@@ -16,6 +17,8 @@
   \item{target}{target return value}
 
   \item{alpha}{alpha value for ETL/ES/CVaR}
+
+  \item{solver}{solver to use}
 }
 \description{
   This function is called by optimize.portfolio to solve

Modified: pkg/PortfolioAnalytics/man/gmv_opt.Rd
===================================================================
--- pkg/PortfolioAnalytics/man/gmv_opt.Rd	2014-01-11 21:23:39 UTC (rev 3299)
+++ pkg/PortfolioAnalytics/man/gmv_opt.Rd	2014-01-11 21:26:56 UTC (rev 3300)
@@ -1,9 +1,9 @@
 \name{gmv_opt}
 \alias{gmv_opt}
-\title{Optimization function to solve minimum variance or maximum quadratic utility problems}
+\title{GMV/QU QP Optimization}
 \usage{
   gmv_opt(R, constraints, moments, lambda, target,
-    lambda_hhi, conc_groups)
+    lambda_hhi, conc_groups, solver = "quadprog")
 }
 \arguments{
   \item{R}{xts object of asset returns}
@@ -22,6 +22,8 @@
 
   \item{conc_groups}{list of vectors specifying the groups
   of the assets.}
+
+  \item{solver}{solver to use}
 }
 \description{
   This function is called by optimize.portfolio to solve

Added: pkg/PortfolioAnalytics/man/gmv_opt_ptc.Rd
===================================================================
--- pkg/PortfolioAnalytics/man/gmv_opt_ptc.Rd	                        (rev 0)
+++ pkg/PortfolioAnalytics/man/gmv_opt_ptc.Rd	2014-01-11 21:26:56 UTC (rev 3300)
@@ -0,0 +1,33 @@
+\name{gmv_opt_ptc}
+\alias{gmv_opt_ptc}
+\title{GMV/QU QP Optimization with Proportional Transaction Cost Constraint}
+\usage{
+  gmv_opt_ptc(R, constraints, moments, lambda, target,
+    init_weights, solver = "quadprog")
+}
+\arguments{
+  \item{R}{xts object of asset returns}
+
+  \item{constraints}{object of constraints in the portfolio
+  object extracted with \code{get_constraints}}
+
+  \item{moments}{object of moments computed based on
+  objective functions}
+
+  \item{lambda}{risk_aversion parameter}
+
+  \item{target}{target return value}
+
+  \item{init_weights}{initial weights to compute turnover}
+
+  \item{solver}{solver to use}
+}
+\description{
+  This function is called by optimize.portfolio to solve
+  minimum variance or maximum quadratic utility problems
+  with proportional transaction cost constraint
+}
+\author{
+  Ross Bennett
+}
+

Modified: pkg/PortfolioAnalytics/man/gmv_opt_toc.Rd
===================================================================
--- pkg/PortfolioAnalytics/man/gmv_opt_toc.Rd	2014-01-11 21:23:39 UTC (rev 3299)
+++ pkg/PortfolioAnalytics/man/gmv_opt_toc.Rd	2014-01-11 21:26:56 UTC (rev 3300)
@@ -1,9 +1,9 @@
 \name{gmv_opt_toc}
 \alias{gmv_opt_toc}
-\title{Optimization function to solve minimum variance or maximum quadratic utility problems with turnover constraint}
+\title{GMV/QU QP Optimization with Turnover Constraint}
 \usage{
   gmv_opt_toc(R, constraints, moments, lambda, target,
-    init_weights)
+    init_weights, solver = "quadprog")
 }
 \arguments{
   \item{R}{xts object of asset returns}
@@ -19,10 +19,13 @@
   \item{target}{target return value}
 
   \item{init_weights}{initial weights to compute turnover}
+
+  \item{solver}{solver to use}
 }
 \description{
   This function is called by optimize.portfolio to solve
   minimum variance or maximum quadratic utility problems
+  with turnover constraint
 }
 \author{
   Ross Bennett

Modified: pkg/PortfolioAnalytics/man/maxret_milp_opt.Rd
===================================================================
--- pkg/PortfolioAnalytics/man/maxret_milp_opt.Rd	2014-01-11 21:23:39 UTC (rev 3299)
+++ pkg/PortfolioAnalytics/man/maxret_milp_opt.Rd	2014-01-11 21:26:56 UTC (rev 3300)
@@ -1,8 +1,9 @@
 \name{maxret_milp_opt}
 \alias{maxret_milp_opt}
-\title{Optimization function to solve maximum return problems}
+\title{Maximum Return MILP Optimization}
 \usage{
-  maxret_milp_opt(R, constraints, moments, target)
+  maxret_milp_opt(R, constraints, moments, target,
+    solver = "glpk")
 }
 \arguments{
   \item{R}{xts object of asset returns}
@@ -14,6 +15,8 @@
   objective functions}
 
   \item{target}{target return value}
+
+  \item{solver}{solver to use}
 }
 \description{
   This function is called by optimize.portfolio to solve

Modified: pkg/PortfolioAnalytics/man/maxret_opt.Rd
===================================================================
--- pkg/PortfolioAnalytics/man/maxret_opt.Rd	2014-01-11 21:23:39 UTC (rev 3299)
+++ pkg/PortfolioAnalytics/man/maxret_opt.Rd	2014-01-11 21:26:56 UTC (rev 3300)
@@ -1,8 +1,9 @@
 \name{maxret_opt}
 \alias{maxret_opt}
-\title{Optimization function to solve minimum variance or maximum quadratic utility problems}
+\title{Maximum Return LP Optimization}
 \usage{
-  maxret_opt(R, moments, constraints, target)
+  maxret_opt(R, moments, constraints, target,
+    solver = "glpk")
 }
 \arguments{
   \item{R}{xts object of asset returns}
@@ -14,10 +15,12 @@
   objective functions}
 
   \item{target}{target return value}
+
+  \item{solver}{solver to use}
 }
 \description{
   This function is called by optimize.portfolio to solve
-  minimum variance or maximum quadratic utility problems
+  maximum return
 }
 \author{
   Ross Bennett

Modified: pkg/PortfolioAnalytics/man/optimize.portfolio.Rd
===================================================================
--- pkg/PortfolioAnalytics/man/optimize.portfolio.Rd	2014-01-11 21:23:39 UTC (rev 3299)
+++ pkg/PortfolioAnalytics/man/optimize.portfolio.Rd	2014-01-11 21:26:56 UTC (rev 3300)
@@ -11,13 +11,13 @@
 
   optimize.portfolio_v2(R, portfolio = NULL,
     constraints = NULL, objectives = NULL,
-    optimize_method = c("DEoptim", "random", "ROI", "ROI_old", "pso", "GenSA"),
+    optimize_method = c("DEoptim", "random", "ROI", "pso", "GenSA"),
     search_size = 20000, trace = FALSE, ..., rp = NULL,
     momentFUN = "set.portfolio.moments", message = FALSE)
 
   optimize.portfolio(R, portfolio = NULL,
     constraints = NULL, objectives = NULL,
-    optimize_method = c("DEoptim", "random", "ROI", "ROI_old", "pso", "GenSA"),
+    optimize_method = c("DEoptim", "random", "ROI", "pso", "GenSA"),
     search_size = 20000, trace = FALSE, ..., rp = NULL,
     momentFUN = "set.portfolio.moments", message = FALSE)
 }
@@ -35,13 +35,9 @@
   \item{objectives}{default=NULL, a list of objective
   objects.}
 
-  \item{optimize_method}{one of "DEoptim", "random",
-  "ROI","ROI_old", "pso", "GenSA".  For using
-  \code{ROI_old}, need to use a constraint_ROI object in
-  constraints. For using \code{ROI}, pass standard
-  \code{constratint} object in \code{constraints} argument.
-  Presently, ROI has plugins for \code{quadprog} and
-  \code{Rglpk}.}
+  \item{optimize_method}{one of "DEoptim", "random", "ROI",
+  "pso", "GenSA". A solver for ROI can also be specified
+  and will be solved using ROI. See Details.}
 
   \item{search_size}{integer, how many portfolios to test,
   default 20,000}
@@ -151,6 +147,15 @@
   When using GenSA and want to set \code{verbose=TRUE},
   instead use \code{trace}.
 
+  If \code{optimize_method="ROI"} is specified, a default
+  solver will be selected based on the optimization
+  problem. The \code{glpk} solver is the default solver for
+  LP and MILP optimization problems. The \code{quadprog}
+  solver is the default solver for QP optimization
+  problems. For example, \code{optimize_method =
+  "quadprog"} can be specified and the optimization problem
+  will be solved via ROI using the quadprog solver.
+
   The extension to ROI solves a limited type of convex
   optimization problems: \itemize{ \item{Maxmimize
   portfolio return subject leverage, box, group, position


From noreply at r-forge.r-project.org  Sat Jan 18 16:26:12 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Sat, 18 Jan 2014 16:26:12 +0100 (CET)
Subject: [Returnanalytics-commits] r3301 - in pkg/PerformanceAnalytics: . R
	sandbox/Shubhankit/noniid.sm/R sandbox/pulkit/R
Message-ID: <20140118152613.0D5F9186C4B@r-forge.r-project.org>

Author: braverock
Date: 2014-01-18 16:26:12 +0100 (Sat, 18 Jan 2014)
New Revision: 3301

Added:
   pkg/PerformanceAnalytics/R/lpm.R
Modified:
   pkg/PerformanceAnalytics/DESCRIPTION
   pkg/PerformanceAnalytics/R/ActivePremium.R
   pkg/PerformanceAnalytics/R/AdjustedSharpeRatio.R
   pkg/PerformanceAnalytics/R/AppraisalRatio.R
   pkg/PerformanceAnalytics/R/BernadoLedoitratio.R
   pkg/PerformanceAnalytics/R/BurkeRatio.R
   pkg/PerformanceAnalytics/R/CAPM.alpha.R
   pkg/PerformanceAnalytics/R/CAPM.beta.R
   pkg/PerformanceAnalytics/R/CAPM.epsilon.R
   pkg/PerformanceAnalytics/R/CAPM.jensenAlpha.R
   pkg/PerformanceAnalytics/R/CAPM.utils.R
   pkg/PerformanceAnalytics/R/CalmarRatio.R
   pkg/PerformanceAnalytics/R/CoMoments.R
   pkg/PerformanceAnalytics/R/DRatio.R
   pkg/PerformanceAnalytics/R/DownsideDeviation.R
   pkg/PerformanceAnalytics/R/DownsideFrequency.R
   pkg/PerformanceAnalytics/R/DrawdownPeak.R
   pkg/PerformanceAnalytics/R/Drawdowns.R
   pkg/PerformanceAnalytics/R/ES.R
   pkg/PerformanceAnalytics/R/FamaBeta.R
   pkg/PerformanceAnalytics/R/Frequency.R
   pkg/PerformanceAnalytics/R/HerfindahlIndex.R
   pkg/PerformanceAnalytics/R/HurstIndex.R
   pkg/PerformanceAnalytics/R/InformationRatio.R
   pkg/PerformanceAnalytics/R/Kappa.R
   pkg/PerformanceAnalytics/R/KellyRatio.R
   pkg/PerformanceAnalytics/R/M2Sortino.R
   pkg/PerformanceAnalytics/R/MSquared.R
   pkg/PerformanceAnalytics/R/MSquaredExcess.R
   pkg/PerformanceAnalytics/R/MartinRatio.R
   pkg/PerformanceAnalytics/R/MeanAbsoluteDeviation.R
   pkg/PerformanceAnalytics/R/MultivariateMoments.R
   pkg/PerformanceAnalytics/R/NetSelectivity.R
   pkg/PerformanceAnalytics/R/Omega.R
   pkg/PerformanceAnalytics/R/OmegaExcessReturn.R
   pkg/PerformanceAnalytics/R/OmegaSharpeRatio.R
   pkg/PerformanceAnalytics/R/PainIndex.R
   pkg/PerformanceAnalytics/R/PainRatio.R
   pkg/PerformanceAnalytics/R/PortfolioRisk.R
   pkg/PerformanceAnalytics/R/ProspectRatio.R
   pkg/PerformanceAnalytics/R/Return.Geltner.R
   pkg/PerformanceAnalytics/R/Return.annualized.R
   pkg/PerformanceAnalytics/R/Return.calculate.R
   pkg/PerformanceAnalytics/R/Return.clean.R
   pkg/PerformanceAnalytics/R/Return.cumulative.R
   pkg/PerformanceAnalytics/R/Return.excess.R
   pkg/PerformanceAnalytics/R/Return.index.R
   pkg/PerformanceAnalytics/R/Return.portfolio.R
   pkg/PerformanceAnalytics/R/Return.read.R
   pkg/PerformanceAnalytics/R/Return.relative.R
   pkg/PerformanceAnalytics/R/Return.wealthindex.R
   pkg/PerformanceAnalytics/R/Selectivity.R
   pkg/PerformanceAnalytics/R/SemiDeviation.R
   pkg/PerformanceAnalytics/R/SharpeRatio.R
   pkg/PerformanceAnalytics/R/SharpeRatio.annualized.R
   pkg/PerformanceAnalytics/R/SkewnessKurtosisRatio.R
   pkg/PerformanceAnalytics/R/SmoothingIndex.R
   pkg/PerformanceAnalytics/R/SortinoRatio.R
   pkg/PerformanceAnalytics/R/SpecificRisk.R
   pkg/PerformanceAnalytics/R/StdDev.R
   pkg/PerformanceAnalytics/R/StdDev.annualized.R
   pkg/PerformanceAnalytics/R/SystematicRisk.R
   pkg/PerformanceAnalytics/R/TotalRisk.R
   pkg/PerformanceAnalytics/R/TrackingError.R
   pkg/PerformanceAnalytics/R/TreynorRatio.R
   pkg/PerformanceAnalytics/R/UlcerIndex.R
   pkg/PerformanceAnalytics/R/UpDownRatios.R
   pkg/PerformanceAnalytics/R/UpsideFrequency.R
   pkg/PerformanceAnalytics/R/UpsidePotentialRatio.R
   pkg/PerformanceAnalytics/R/UpsideRisk.R
   pkg/PerformanceAnalytics/R/VaR.Marginal.R
   pkg/PerformanceAnalytics/R/VaR.R
   pkg/PerformanceAnalytics/R/VolatilitySkewness.R
   pkg/PerformanceAnalytics/R/apply.fromstart.R
   pkg/PerformanceAnalytics/R/apply.rolling.R
   pkg/PerformanceAnalytics/R/chart.ACF.R
   pkg/PerformanceAnalytics/R/chart.ACFplus.R
   pkg/PerformanceAnalytics/R/chart.Bar.R
   pkg/PerformanceAnalytics/R/chart.BarVaR.R
   pkg/PerformanceAnalytics/R/chart.Boxplot.R
   pkg/PerformanceAnalytics/R/chart.CaptureRatios.R
   pkg/PerformanceAnalytics/R/chart.Correlation.R
   pkg/PerformanceAnalytics/R/chart.CumReturns.R
   pkg/PerformanceAnalytics/R/chart.Drawdown.R
   pkg/PerformanceAnalytics/R/chart.ECDF.R
   pkg/PerformanceAnalytics/R/chart.Events.R
   pkg/PerformanceAnalytics/R/chart.Histogram.R
   pkg/PerformanceAnalytics/R/chart.QQPlot.R
   pkg/PerformanceAnalytics/R/chart.Regression.R
   pkg/PerformanceAnalytics/R/chart.RelativePerformance.R
   pkg/PerformanceAnalytics/R/chart.RiskReturnScatter.R
   pkg/PerformanceAnalytics/R/chart.RollingCorrelation.R
   pkg/PerformanceAnalytics/R/chart.RollingMean.R
   pkg/PerformanceAnalytics/R/chart.RollingPerformance.R
   pkg/PerformanceAnalytics/R/chart.RollingQuantileRegression.R
   pkg/PerformanceAnalytics/R/chart.RollingRegression.R
   pkg/PerformanceAnalytics/R/chart.Scatter.R
   pkg/PerformanceAnalytics/R/chart.SnailTrail.R
   pkg/PerformanceAnalytics/R/chart.StackedBar.R
   pkg/PerformanceAnalytics/R/chart.TimeSeries.R
   pkg/PerformanceAnalytics/R/chart.VaRSensitivity.R
   pkg/PerformanceAnalytics/R/charts.Bar.R
   pkg/PerformanceAnalytics/R/charts.BarVaR.R
   pkg/PerformanceAnalytics/R/charts.PerformanceSummary.R
   pkg/PerformanceAnalytics/R/charts.RollingPerformance.R
   pkg/PerformanceAnalytics/R/charts.RollingRegression.R
   pkg/PerformanceAnalytics/R/charts.TimeSeries.R
   pkg/PerformanceAnalytics/R/checkData.R
   pkg/PerformanceAnalytics/R/decomposeMVaR.R
   pkg/PerformanceAnalytics/R/findDrawdowns.R
   pkg/PerformanceAnalytics/R/kurtosis.R
   pkg/PerformanceAnalytics/R/legend.R
   pkg/PerformanceAnalytics/R/maxDrawdown.R
   pkg/PerformanceAnalytics/R/mean.utils.R
   pkg/PerformanceAnalytics/R/na.skip.R
   pkg/PerformanceAnalytics/R/rCornishFisher.r
   pkg/PerformanceAnalytics/R/replaceTabs.R
   pkg/PerformanceAnalytics/R/skewness.R
   pkg/PerformanceAnalytics/R/sortDrawdowns.R
   pkg/PerformanceAnalytics/R/table.AnnualizedReturns.R
   pkg/PerformanceAnalytics/R/table.Arbitrary.R
   pkg/PerformanceAnalytics/R/table.Autocorrelation.R
   pkg/PerformanceAnalytics/R/table.CAPM.R
   pkg/PerformanceAnalytics/R/table.CalendarReturns.R
   pkg/PerformanceAnalytics/R/table.CaptureRatios.R
   pkg/PerformanceAnalytics/R/table.Correlation.R
   pkg/PerformanceAnalytics/R/table.Distributions.R
   pkg/PerformanceAnalytics/R/table.DownsideRisk.R
   pkg/PerformanceAnalytics/R/table.DownsideRiskRatio.R
   pkg/PerformanceAnalytics/R/table.Drawdowns.R
   pkg/PerformanceAnalytics/R/table.DrawdownsRatio.R
   pkg/PerformanceAnalytics/R/table.HigherMoments.R
   pkg/PerformanceAnalytics/R/table.InformationRatio.R
   pkg/PerformanceAnalytics/R/table.MonthlyReturns.R
   pkg/PerformanceAnalytics/R/table.RollingPeriods.R
   pkg/PerformanceAnalytics/R/table.SpecificRisk.R
   pkg/PerformanceAnalytics/R/table.UpDownRatios.R
   pkg/PerformanceAnalytics/R/table.Variability.R
   pkg/PerformanceAnalytics/R/textplot.R
   pkg/PerformanceAnalytics/R/valueAtRiskFunctions.r
   pkg/PerformanceAnalytics/R/zerofill.R
   pkg/PerformanceAnalytics/R/zzz.R
   pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/CDrawdown.R
   pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/GLMSmoothIndex.R
   pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/Return.GLM.R
   pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/Return.Okunev.R
   pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/chart.AcarSim.R
   pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/chart.Autocorrelation.R
   pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/na.skip.R
   pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/table.EMaxDDGBM.R
   pkg/PerformanceAnalytics/sandbox/pulkit/R/EDDCOPS.R
   pkg/PerformanceAnalytics/sandbox/pulkit/R/Edd.R
   pkg/PerformanceAnalytics/sandbox/pulkit/R/MinTRL.R
   pkg/PerformanceAnalytics/sandbox/pulkit/R/ProbSharpeRatio.R
   pkg/PerformanceAnalytics/sandbox/pulkit/R/REDDCOPS.R
   pkg/PerformanceAnalytics/sandbox/pulkit/R/REM.R
   pkg/PerformanceAnalytics/sandbox/pulkit/R/na.skip.R
   pkg/PerformanceAnalytics/sandbox/pulkit/R/redd.R
Log:
- update copyright to 2014


Modified: pkg/PerformanceAnalytics/DESCRIPTION
===================================================================
--- pkg/PerformanceAnalytics/DESCRIPTION	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/DESCRIPTION	2014-01-18 15:26:12 UTC (rev 3301)
@@ -30,7 +30,7 @@
     ff
 License: GPL
 URL: http://r-forge.r-project.org/projects/returnanalytics/
-Copyright: (c) 2004-2012
+Copyright: (c) 2004-2014
 Contributors: Kris Boudt,  Diethelm Wuertz, Eric Zivot, Matthieu Lestel
 Thanks: A special thanks for additional contributions from
     Stefan Albrecht, Khahn Nygyen, Jeff Ryan,

Modified: pkg/PerformanceAnalytics/R/ActivePremium.R
===================================================================
--- pkg/PerformanceAnalytics/R/ActivePremium.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/ActivePremium.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -77,7 +77,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/AdjustedSharpeRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/AdjustedSharpeRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/AdjustedSharpeRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -67,7 +67,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/AppraisalRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/AppraisalRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/AppraisalRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -94,7 +94,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/BernadoLedoitratio.R
===================================================================
--- pkg/PerformanceAnalytics/R/BernadoLedoitratio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/BernadoLedoitratio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -49,7 +49,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/BurkeRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/BurkeRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/BurkeRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -129,7 +129,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/CAPM.alpha.R
===================================================================
--- pkg/PerformanceAnalytics/R/CAPM.alpha.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/CAPM.alpha.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -98,7 +98,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/CAPM.beta.R
===================================================================
--- pkg/PerformanceAnalytics/R/CAPM.beta.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/CAPM.beta.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -264,7 +264,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/CAPM.epsilon.R
===================================================================
--- pkg/PerformanceAnalytics/R/CAPM.epsilon.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/CAPM.epsilon.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -71,7 +71,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/CAPM.jensenAlpha.R
===================================================================
--- pkg/PerformanceAnalytics/R/CAPM.jensenAlpha.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/CAPM.jensenAlpha.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -70,7 +70,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/CAPM.utils.R
===================================================================
--- pkg/PerformanceAnalytics/R/CAPM.utils.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/CAPM.utils.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -156,7 +156,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/CalmarRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/CalmarRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/CalmarRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -119,7 +119,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/CoMoments.R
===================================================================
--- pkg/PerformanceAnalytics/R/CoMoments.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/CoMoments.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -509,7 +509,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/DRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/DRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/DRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -60,7 +60,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/DownsideDeviation.R
===================================================================
--- pkg/PerformanceAnalytics/R/DownsideDeviation.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/DownsideDeviation.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -186,7 +186,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/DownsideFrequency.R
===================================================================
--- pkg/PerformanceAnalytics/R/DownsideFrequency.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/DownsideFrequency.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -64,7 +64,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/DrawdownPeak.R
===================================================================
--- pkg/PerformanceAnalytics/R/DrawdownPeak.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/DrawdownPeak.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -63,7 +63,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Drawdowns.R
===================================================================
--- pkg/PerformanceAnalytics/R/Drawdowns.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Drawdowns.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -39,7 +39,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/ES.R
===================================================================
--- pkg/PerformanceAnalytics/R/ES.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/ES.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -236,7 +236,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/FamaBeta.R
===================================================================
--- pkg/PerformanceAnalytics/R/FamaBeta.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/FamaBeta.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -68,7 +68,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Frequency.R
===================================================================
--- pkg/PerformanceAnalytics/R/Frequency.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Frequency.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -60,7 +60,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/HerfindahlIndex.R
===================================================================
--- pkg/PerformanceAnalytics/R/HerfindahlIndex.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/HerfindahlIndex.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -28,7 +28,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/HurstIndex.R
===================================================================
--- pkg/PerformanceAnalytics/R/HurstIndex.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/HurstIndex.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -58,7 +58,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/InformationRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/InformationRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/InformationRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -81,7 +81,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Kappa.R
===================================================================
--- pkg/PerformanceAnalytics/R/Kappa.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Kappa.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -73,7 +73,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/KellyRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/KellyRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/KellyRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -74,7 +74,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/M2Sortino.R
===================================================================
--- pkg/PerformanceAnalytics/R/M2Sortino.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/M2Sortino.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -72,7 +72,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/MSquared.R
===================================================================
--- pkg/PerformanceAnalytics/R/MSquared.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/MSquared.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -69,7 +69,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/MSquaredExcess.R
===================================================================
--- pkg/PerformanceAnalytics/R/MSquaredExcess.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/MSquaredExcess.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -74,7 +74,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/MartinRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/MartinRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/MartinRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -66,7 +66,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/MeanAbsoluteDeviation.R
===================================================================
--- pkg/PerformanceAnalytics/R/MeanAbsoluteDeviation.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/MeanAbsoluteDeviation.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -47,7 +47,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/MultivariateMoments.R
===================================================================
--- pkg/PerformanceAnalytics/R/MultivariateMoments.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/MultivariateMoments.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -8,7 +8,7 @@
 # efficient when running against very large numbers of instruments or portfolios.
 #
 # Copyright (c) 2008 Kris Boudt and Brian G. Peterson
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson for PerformanceAnalytics
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson for PerformanceAnalytics
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING
 ###############################################################################
@@ -142,7 +142,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson and Kris Boudt
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson and Kris Boudt
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/NetSelectivity.R
===================================================================
--- pkg/PerformanceAnalytics/R/NetSelectivity.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/NetSelectivity.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -71,7 +71,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Omega.R
===================================================================
--- pkg/PerformanceAnalytics/R/Omega.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Omega.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -184,7 +184,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/OmegaExcessReturn.R
===================================================================
--- pkg/PerformanceAnalytics/R/OmegaExcessReturn.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/OmegaExcessReturn.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -73,7 +73,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/OmegaSharpeRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/OmegaSharpeRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/OmegaSharpeRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -78,7 +78,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/PainIndex.R
===================================================================
--- pkg/PerformanceAnalytics/R/PainIndex.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/PainIndex.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -64,7 +64,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/PainRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/PainRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/PainRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -66,7 +66,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/PortfolioRisk.R
===================================================================
--- pkg/PerformanceAnalytics/R/PortfolioRisk.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/PortfolioRisk.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -606,7 +606,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson and Kris Boudt
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson and Kris Boudt
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/ProspectRatio.R
===================================================================
--- pkg/PerformanceAnalytics/R/ProspectRatio.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/ProspectRatio.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -66,7 +66,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Return.Geltner.R
===================================================================
--- pkg/PerformanceAnalytics/R/Return.Geltner.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Return.Geltner.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -84,7 +84,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Return.annualized.R
===================================================================
--- pkg/PerformanceAnalytics/R/Return.annualized.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Return.annualized.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -97,7 +97,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Return.calculate.R
===================================================================
--- pkg/PerformanceAnalytics/R/Return.calculate.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Return.calculate.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -91,7 +91,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Return.clean.R
===================================================================
--- pkg/PerformanceAnalytics/R/Return.clean.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Return.clean.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -258,7 +258,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Return.cumulative.R
===================================================================
--- pkg/PerformanceAnalytics/R/Return.cumulative.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Return.cumulative.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -52,7 +52,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Return.excess.R
===================================================================
--- pkg/PerformanceAnalytics/R/Return.excess.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Return.excess.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -90,7 +90,7 @@
 ###############################################################################
 # R (http://r-project.org/) Econometrics for Performance and Risk Analysis
 #
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+# Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson
 #
 # This R package is distributed under the terms of the GNU Public License (GPL)
 # for full details see the file COPYING

Modified: pkg/PerformanceAnalytics/R/Return.index.R
===================================================================
--- pkg/PerformanceAnalytics/R/Return.index.R	2014-01-11 21:26:56 UTC (rev 3300)
+++ pkg/PerformanceAnalytics/R/Return.index.R	2014-01-18 15:26:12 UTC (rev 3301)
@@ -44,7 +44,7 @@
 ###############################################################################
[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/returnanalytics -r 3301

From noreply at r-forge.r-project.org  Sun Jan 19 20:52:42 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Sun, 19 Jan 2014 20:52:42 +0100 (CET)
Subject: [Returnanalytics-commits] r3302 - in pkg/PortfolioAnalytics: . R
	sandbox
Message-ID: <20140119195242.9236A186907@r-forge.r-project.org>

Author: braverock
Date: 2014-01-19 20:52:42 +0100 (Sun, 19 Jan 2014)
New Revision: 3302

Modified:
   pkg/PortfolioAnalytics/DESCRIPTION
   pkg/PortfolioAnalytics/R/applyFUN.R
   pkg/PortfolioAnalytics/R/chart.RiskReward.R
   pkg/PortfolioAnalytics/R/chart.Weights.R
   pkg/PortfolioAnalytics/R/charts.DE.R
   pkg/PortfolioAnalytics/R/charts.GenSA.R
   pkg/PortfolioAnalytics/R/charts.PSO.R
   pkg/PortfolioAnalytics/R/charts.ROI.R
   pkg/PortfolioAnalytics/R/charts.RP.R
   pkg/PortfolioAnalytics/R/charts.efficient.frontier.R
   pkg/PortfolioAnalytics/R/charts.groups.R
   pkg/PortfolioAnalytics/R/charts.multiple.R
   pkg/PortfolioAnalytics/R/charts.risk.R
   pkg/PortfolioAnalytics/R/constrained_objective.R
   pkg/PortfolioAnalytics/R/constraint_fn_map.R
   pkg/PortfolioAnalytics/R/constraints.R
   pkg/PortfolioAnalytics/R/constraintsFUN.R
   pkg/PortfolioAnalytics/R/constraints_ROI.R
   pkg/PortfolioAnalytics/R/equal.weight.R
   pkg/PortfolioAnalytics/R/extract.efficient.frontier.R
   pkg/PortfolioAnalytics/R/extractstats.R
   pkg/PortfolioAnalytics/R/generics.R
   pkg/PortfolioAnalytics/R/inverse.volatility.weight.R
   pkg/PortfolioAnalytics/R/moment.functions.R
   pkg/PortfolioAnalytics/R/objective.R
   pkg/PortfolioAnalytics/R/objectiveFUN.R
   pkg/PortfolioAnalytics/R/optFUN.R
   pkg/PortfolioAnalytics/R/optimize.portfolio.R
   pkg/PortfolioAnalytics/R/portfolio.R
   pkg/PortfolioAnalytics/R/random_portfolios.R
   pkg/PortfolioAnalytics/R/trailingFUN.R
   pkg/PortfolioAnalytics/R/utility.combine.R
   pkg/PortfolioAnalytics/R/utils.R
   pkg/PortfolioAnalytics/sandbox/TAA.R
   pkg/PortfolioAnalytics/sandbox/applylocalsearch.R
   pkg/PortfolioAnalytics/sandbox/localsearch.R
   pkg/PortfolioAnalytics/sandbox/optimizer.R
Log:
- update Copyright to 2014, bump version


Modified: pkg/PortfolioAnalytics/DESCRIPTION
===================================================================
--- pkg/PortfolioAnalytics/DESCRIPTION	2014-01-18 15:26:12 UTC (rev 3301)
+++ pkg/PortfolioAnalytics/DESCRIPTION	2014-01-19 19:52:42 UTC (rev 3302)
@@ -1,8 +1,8 @@
 Package: PortfolioAnalytics
 Type: Package
-Title: Portfolio Analysis, including Numeric Methods for Optimization
+Title: Portfolio Analysis, including Numerical Methods for Optimization
     of Portfolios
-Version: 0.8.3
+Version: 0.9.0
 Date: $Date$
 Author: Brian G. Peterson, Peter Carl, Ross Bennett, Kris Boudt
 Contributors: R. Douglas Martin, Guy Yollin, Hezky Varon
@@ -27,9 +27,10 @@
     ROI.plugin.symphony (>= 0.0.2),
     pso,
     GenSA,
-    corpcor
+    corpcor,
+    testthat
 License: GPL
-Copyright: (c) 2004-2012
+Copyright: (c) 2004-2014
 Collate:
     'charts.DE.R'
     'charts.RP.R'

Modified: pkg/PortfolioAnalytics/R/applyFUN.R
===================================================================
--- pkg/PortfolioAnalytics/R/applyFUN.R	2014-01-18 15:26:12 UTC (rev 3301)
+++ pkg/PortfolioAnalytics/R/applyFUN.R	2014-01-19 19:52:42 UTC (rev 3302)
@@ -154,3 +154,4116 @@
   }
   return(out)
 }
+
+#' classic risk reward scatter
+#' 
+#' This function charts the \code{optimize.portfolio} object in risk-return space.
+#' 
+#' @details
+#' \code{neighbors} may be specified in three ways.  
+#' The first is as a single number of neighbors.  This will extract the \code{neighbors} closest 
+#' portfolios in terms of the \code{out} numerical statistic.
+#' The second method consists of a numeric vector for \code{neighbors}.
+#' This will extract the \code{neighbors} with portfolio index numbers that correspond to the vector contents.
+#' The third method for specifying \code{neighbors} is to pass in a matrix.  
+#' This matrix should look like the output of \code{\link{extractStats}}, and should contain
+#' \code{risk.col},\code{return.col}, and weights columns all properly named. 
+#' 
+#' @param object optimal portfolio created by \code{\link{optimize.portfolio}}.
+#' @param neighbors set of 'neighbor' portfolios to overplot, see Details.
+#' @param \dots any other passthru parameters.
+#' @param return.col string matching the objective of a 'return' objective, on vertical axis.
+#' @param risk.col string matching the objective of a 'risk' objective, on horizontal axis.
+#' @param chart.assets TRUE/FALSE. Includes a risk reward scatter of the assets in the chart.
+#' @param element.color color for the default plot scatter points.
+#' @param cex.axis The magnification to be used for axis annotation relative to the current setting of \code{cex}.
+#' @param xlim set the x-axis limit, same as in \code{\link{plot}}.
+#' @param ylim set the y-axis limit, same as in \code{\link{plot}}.
+#' @param rp TRUE/FALSE to generate random portfolios to plot the feasible space
+#' @param main a main title for the plot.
+#' @param labels.assets TRUE/FALSE to include the names in the plot. 
+#' @param pch.assets plotting character of the assets, same as in \code{\link{plot}}
+#' @param cex.assets numerical value giving the amount by which the asset points should be magnified relative to the default.
+#' @param cex.lab numerical value giving the amount by which the labels should be magnified relative to the default.
+#' @param colorset color palette or vector of colors to use.
+#' @seealso \code{\link{optimize.portfolio}}
+#' @export
+chart.RiskReward <- function(object, ...){
+  UseMethod("chart.RiskReward")
+}
+
+
+#' Chart the efficient frontier and risk-return scatter
+#' 
+#' Chart the efficient frontier and risk-return scatter of the assets for 
+#' \code{optimize.portfolio} or \code{efficient.frontier} objects
+#' 
+#' @details
+#' For objects created by optimize.portfolio with 'DEoptim', 'random', or 'pso'
+#' specified as the optimize_method:
+#' \itemize{
+#'   \item The efficient frontier plotted is based on the the trace information (sets of 
+#'   portfolios tested by the solver at each iteration) in objects created by 
+#'   \code{optimize.portfolio}.
+#' }
+#' 
+#' For objects created by optimize.portfolio with 'ROI' specified as the 
+#' optimize_method:
+#' \itemize{
+#'   \item The mean-StdDev or mean-ETL efficient frontier can be plotted for optimal
+#'   portfolio objects created by \code{optimize.portfolio}.
+#' 
+#'   \item If \code{match.col="StdDev"}, the mean-StdDev efficient frontier is plotted.
+#' 
+#'   \item If \code{match.col="ETL"} (also "ES" or "CVaR"), the mean-ETL efficient frontier is plotted.
+#' }
+#' 
+#' Note that \code{trace=TRUE} must be specified in \code{\link{optimize.portfolio}}
+#' 
+#' GenSA does not return any useable trace information for portfolios tested at 
+#' each iteration, therfore we cannot extract and chart an efficient frontier.
+#' 
+#' By default, the tangency portfolio (maximum Sharpe Ratio or modified Sharpe Ratio)
+#' will be plotted using a risk free rate of 0. Set \code{rf=NULL} to omit 
+#' this from the plot. 
+#' 
+#' @param object object to chart.
+#' @param \dots passthru parameters to \code{\link{plot}}
+#' @param match.col string name of column to use for risk (horizontal axis).
+#' \code{match.col} must match the name of an objective measure in the 
+#' \code{objective_measures} or \code{opt_values} slot in the object created 
+#' by \code{\link{optimize.portfolio}}.
+#' @param n.portfolios number of portfolios to use to plot the efficient frontier.
+#' @param xlim set the x-axis limit, same as in \code{\link{plot}}.
+#' @param ylim set the y-axis limit, same as in \code{\link{plot}}.
+#' @param cex.axis numerical value giving the amount by which the axis should be magnified relative to the default.
+#' @param element.color provides the color for drawing less-important chart elements, such as the box lines, axis lines, etc.
+#' @param main a main title for the plot.
+#' @param RAR.text string name for risk adjusted return text to plot in the legend.
+#' @param rf risk free rate. If \code{rf} is not null, the maximum Sharpe Ratio or modified Sharpe Ratio tangency portfolio will be plotted.
+#' @param tangent.line TRUE/FALSE to plot the tangent line.
+#' @param cex.legend numerical value giving the amount by which the legend should be magnified relative to the default.
+#' @param chart.assets TRUE/FALSE to include the assets.
+#' @param labels.assets TRUE/FALSE to include the asset names in the plot. 
+#' \code{chart.assets} must be \code{TRUE} to plot asset names.
+#' @param pch.assets plotting character of the assets, same as in \code{\link{plot}}.
+#' @param cex.assets numerical value giving the amount by which the asset points and labels should be magnified relative to the default.
+#' @author Ross Bennett
+#' @rdname chart.EfficientFrontier
+#' @export
+chart.EfficientFrontier <- function(object, ...){
+  UseMethod("chart.EfficientFrontier")
+}
+
+#' @rdname chart.EfficientFrontier
+#' @method chart.EfficientFrontier optimize.portfolio.ROI
+#' @S3method chart.EfficientFrontier optimize.portfolio.ROI
+chart.EfficientFrontier.optimize.portfolio.ROI <- function(object, ..., match.col="ES", n.portfolios=25, xlim=NULL, ylim=NULL, cex.axis=0.8, element.color="darkgray", main="Efficient Frontier", RAR.text="SR", rf=0, tangent.line=TRUE, cex.legend=0.8, chart.assets=TRUE, labels.assets=TRUE, pch.assets=21, cex.assets=0.8){
+  if(!inherits(object, "optimize.portfolio.ROI")) stop("object must be of class optimize.portfolio.ROI")
+  
+  portf <- object$portfolio
+  R <- object$R
+  if(is.null(R)) stop(paste("Not able to get asset returns from", object))
+  wts <- object$weights
+  objectclass <- class(object)[1]
+  
+  # objnames <- unlist(lapply(portf$objectives, function(x) x$name))
+  # if(!(match.col %in% objnames)){
+  #   stop("match.col must match an objective name")
+  # }
+  
+  # get the optimal return and risk metrics
+  xtract <- extractStats(object=object)
+  columnames <- names(xtract)
+  if(!(("mean") %in% columnames)){
+    # we need to calculate the mean given the optimal weights
+    opt_ret <- applyFUN(R=R, weights=wts, FUN="mean")
+  } else {
+    opt_ret <- xtract["mean"]
+  }
+  # get the match.col column
+  mtc <- pmatch(match.col, columnames)
+  if(is.na(mtc)) {
+    mtc <- pmatch(paste(match.col,match.col,sep='.'), columnames)
+  }
+  if(is.na(mtc)){
+    # if(is.na(mtc)) stop("could not match match.col with column name of extractStats output")
+    opt_risk <- applyFUN(R=R, weights=wts, FUN=match.col)
+  } else {
+    opt_risk <- xtract[mtc]
+  }
+  
+  # get the data to plot scatter of asset returns
+  asset_ret <- scatterFUN(R=R, FUN="mean")
+  asset_risk <- scatterFUN(R=R, FUN=match.col)
+  rnames <- colnames(R)
+  
+  if(match.col %in% c("ETL", "ES", "CVaR")){
+    frontier <- meanetl.efficient.frontier(portfolio=portf, R=R, n.portfolios=n.portfolios)
+    rar <- "STARR"
+  }
+  if(match.col == "StdDev"){
+    frontier <- meanvar.efficient.frontier(portfolio=portf, R=R, n.portfolios=n.portfolios)
+    rar <- "SR"
+  }
+  # data points to plot the frontier
+  x.f <- frontier[, match.col]
+  y.f <- frontier[, "mean"]
+  
+  # Points for the Sharpe Ratio ((mu - rf) / StdDev) or STARR ((mu - rf) / ETL)
+  if(!is.null(rf)){
+    sr <- (y.f - rf) / (x.f)
+    idx.maxsr <- which.max(sr)
+    srmax <- sr[idx.maxsr]
+  }
+  
+  # set the x and y limits
+  if(is.null(xlim)){
+    xlim <- range(c(x.f, asset_risk))
+    # xlim[1] <- xlim[1] * 0.8
+    xlim[1] <- 0
+    xlim[2] <- xlim[2] * 1.15
+  }
+  if(is.null(ylim)){
+    ylim <- range(c(y.f, asset_ret))
+    # ylim[1] <- ylim[1] * 0.9
+    ylim[1] <- 0
+    ylim[2] <- ylim[2] * 1.1
+  }
+  
+  # plot the efficient frontier line
+  plot(x=x.f, y=y.f, ylab="Mean", xlab=match.col, main=main, xlim=xlim, ylim=ylim, axes=FALSE, ...)
+  
+  # Add the global minimum variance or global minimum ETL portfolio
+  points(x=x.f[1], y=y.f[1], pch=16)
+  
+  if(chart.assets){
+    # risk-return scatter of the assets
+    points(x=asset_risk, y=asset_ret, pch=pch.assets, cex=cex.assets)
+    if(labels.assets) text(x=asset_risk, y=asset_ret, labels=rnames, pos=4, cex=cex.assets)
+  }
+  
+  # plot the optimal portfolio
+  points(opt_risk, opt_ret, col="blue", pch=16) # optimal
+  text(x=opt_risk, y=opt_ret, labels="Optimal",col="blue", pos=4, cex=0.8)
+  if(!is.null(rf)){
+    # Plot tangency line and points at risk-free rate and tangency portfolio
+    if(tangent.line) abline(rf, srmax, lty=2)
+    points(0, rf, pch=16)
+    points(x.f[idx.maxsr], y.f[idx.maxsr], pch=16)
+    # text(x=x.f[idx.maxsr], y=y.f[idx.maxsr], labels="T", pos=4, cex=0.8)
+    # Add lengend with max Sharpe Ratio and risk-free rate
+    legend("topleft", paste(RAR.text, " = ", signif(srmax,3), sep = ""), bty = "n", cex=cex.legend)
+    legend("topleft", inset = c(0,0.05), paste("rf = ", signif(rf,3), sep = ""), bty = "n", cex=cex.legend)
+  }
+  axis(1, cex.axis = cex.axis, col = element.color)
+  axis(2, cex.axis = cex.axis, col = element.color)
+  box(col = element.color)
+}
+
+#' @rdname chart.EfficientFrontier
+#' @method chart.EfficientFrontier optimize.portfolio
+#' @S3method chart.EfficientFrontier optimize.portfolio
+chart.EfficientFrontier.optimize.portfolio <- function(object, ..., match.col="ES", n.portfolios=25, xlim=NULL, ylim=NULL, cex.axis=0.8, element.color="darkgray", main="Efficient Frontier", RAR.text="SR", rf=0, tangent.line=TRUE, cex.legend=0.8, chart.assets=TRUE, labels.assets=TRUE, pch.assets=21, cex.assets=0.8){
+  # This function will work with objects of class optimize.portfolio.DEoptim,
+  # optimize.portfolio.random, and optimize.portfolio.pso
+  
+  if(inherits(object, "optimize.portfolio.GenSA")){
+    stop("GenSA does not return any useable trace information for portfolios tested, thus we cannot extract an efficient frontier.")
+  }
+  
+  if(!inherits(object, "optimize.portfolio")) stop("object must be of class optimize.portfolio")
+  
+  portf <- object$portfolio
+  R <- object$R
+  if(is.null(R)) stop(paste("Not able to get asset returns from", object))
+  wts <- object$weights
+  
+  # get the stats from the object
+  xtract <- extractStats(object=object)
+  columnames <- colnames(xtract)
+  
+  # Check if match.col is in extractStats output
+  if(!(match.col %in% columnames)){
+    stop(paste(match.col, "is not a column in extractStats output"))
+  }
+  
+  # check if 'mean' is in extractStats output
+  if(!("mean" %in% columnames)){
+    stop("mean is not a column in extractStats output")
+  }
+  
+  # get the stats of the optimal portfolio
+  optstats <- xtract[which.min(xtract[, "out"]), ]
+  opt_ret <- optstats["mean"]
+  opt_risk <- optstats[match.col]
+  
+  # get the data to plot scatter of asset returns
+  asset_ret <- scatterFUN(R=R, FUN="mean")
+  asset_risk <- scatterFUN(R=R, FUN=match.col)
+  rnames <- colnames(R)
+  
+  # get the data of the efficient frontier
+  frontier <- extract.efficient.frontier(object=object, match.col=match.col, n.portfolios=n.portfolios)
+  
+  # data points to plot the frontier
+  x.f <- frontier[, match.col]
+  y.f <- frontier[, "mean"]
+  
+  # Points for the Sharpe or Modified Sharpe Ratio
+  if(!is.null(rf)){
+    sr <- (y.f - rf) / (x.f)
+    idx.maxsr <- which.max(sr)
+    srmax <- sr[idx.maxsr]
+  }
+  
+  # set the x and y limits
+  if(is.null(xlim)){
+    xlim <- range(c(x.f, asset_risk))
+    # xlim[1] <- xlim[1] * 0.8
+    xlim[1] <- 0
+    xlim[2] <- xlim[2] * 1.15
+  }
+  if(is.null(ylim)){
+    ylim <- range(c(y.f, asset_ret))
+    # ylim[1] <- ylim[1] * 0.9
+    ylim[1] <- 0
+    ylim[2] <- ylim[2] * 1.1
+  }
+  
+  # plot the efficient frontier line
+  plot(x=x.f, y=y.f, ylab="Mean", xlab=match.col, main=main, xlim=xlim, ylim=ylim, axes=FALSE, ...)
+  
+  # Add the global minimum variance or global minimum ETL portfolio
+  points(x=x.f[1], y=y.f[1], pch=16)
+  
+  if(chart.assets){
+    # risk-return scatter of the assets
+    points(x=asset_risk, y=asset_ret, pch=pch.assets, cex=cex.assets)
+    if(labels.assets) text(x=asset_risk, y=asset_ret, labels=rnames, pos=4, cex=cex.assets)
+  }
+  
+  # plot the optimal portfolio
+  points(opt_risk, opt_ret, col="blue", pch=16) # optimal
+  text(x=opt_risk, y=opt_ret, labels="Optimal",col="blue", pos=4, cex=0.8)
+  if(!is.null(rf)){
+    # Plot tangency line and points at risk-free rate and tangency portfolio
+    if(tangent.line) abline(rf, srmax, lty=2)
+    points(0, rf, pch=16)
+    points(x.f[idx.maxsr], y.f[idx.maxsr], pch=16)
+    # text(x=x.f[idx.maxsr], y=y.f[idx.maxsr], labels="T", pos=4, cex=0.8)
+    # Add lengend with max Sharpe Ratio and risk-free rate
+    legend("topleft", paste(RAR.text, " = ", signif(srmax,3), sep = ""), bty = "n", cex=cex.legend)
+    legend("topleft", inset = c(0,0.05), paste("rf = ", signif(rf,3), sep = ""), bty = "n", cex=cex.legend)
+  }
+  axis(1, cex.axis = cex.axis, col = element.color)
+  axis(2, cex.axis = cex.axis, col = element.color)
+  box(col = element.color)
+}
+
+
+#' Chart weights along an efficient frontier
+#' 
+#' This function produces a stacked barplot of weights along an efficient frontier.
+#' 
+#' @param object object of class \code{efficient.frontier} or \code{optimize.portfolio}.
+#' @param \dots passthru parameters to \code{barplot}.
+#' @param colorset color palette or vector of colors to use.
+#' @param n.portfolios number of portfolios to extract along the efficient frontier.
+#' @param by.groups TRUE/FALSE. If TRUE, the group weights are charted.
+#' @param match.col string name of column to use for risk (horizontal axis). Must match the name of an objective.
+#' @param main title used in the plot.
+#' @param cex.lab the magnification to be used for x-axis and y-axis labels relative to the current setting of 'cex'.
+#' @param cex.axis the magnification to be used for sizing the axis text relative to the current setting of 'cex', similar to \code{\link{plot}}.
+#' @param cex.legend the magnification to be used for sizing the legend relative to the current setting of 'cex', similar to \code{\link{plot}}.
+#' @param legend.labels character vector to use for the legend labels.
+#' @param element.color provides the color for drawing less-important chart elements, such as the box lines, axis lines, etc.
+#' @param legend.loc NULL, "topright", "right", or "bottomright". If legend.loc is NULL, the legend will not be plotted.
+#' @author Ross Bennett
+#' @rdname chart.Weights.EF
+#' @export
+chart.Weights.EF <- function(object, ...){
+  UseMethod("chart.Weights.EF")
+}
+
+
+#' @rdname chart.Weights.EF
+#' @method chart.Weights.EF efficient.frontier
+#' @S3method chart.Weights.EF efficient.frontier
+chart.Weights.EF.efficient.frontier <- function(object, ..., colorset=NULL, n.portfolios=25, by.groups=FALSE, match.col="ES", main="", cex.lab=0.8, cex.axis=0.8, cex.legend=0.8, legend.labels=NULL, element.color="darkgray", legend.loc="topright"){
+  # using ideas from weightsPlot.R in fPortfolio package
+  
+  if(!inherits(object, "efficient.frontier")) stop("object must be of class 'efficient.frontier'")
+  
+  if(is.list(object)){
+    # Objects created with create.EfficientFrontier will be a list of 2 elements
+    frontier <- object$frontier
+  } else {
+    # Objects created with extractEfficientFrontier will only be an efficient.frontier object
+    frontier <- object
+  }
+  
+  
+  # get the columns with weights
+  cnames <- colnames(frontier)
+  wts_idx <- grep(pattern="^w\\.", cnames)
+  wts <- frontier[, wts_idx]
+  
+  if(by.groups){
+    constraints <- get_constraints(object$portfolio)
+    groups <- constraints$groups
+    if(is.null(groups)) stop("group constraints not in portfolio object")
+    if(!is.null(groups)){
+      groupfun <- function(weights, groups){
+        # This function is to calculate weights by group given the group list
+        # and a matrix of weights along the efficient frontier
+        ngroups <- length(groups)
+        group_weights <- rep(0, ngroups)
+        for(i in 1:ngroups){
+          group_weights[i] <- sum(weights[groups[[i]]])
+        }
+        group_weights
+      }
+      wts <- t(apply(wts, 1, groupfun, groups=groups))
+    }
+  }
+  
+  # return along the efficient frontier
+  # get the "mean" column
+  mean.mtc <- pmatch("mean", cnames)
+  if(is.na(mean.mtc)) {
+    mean.mtc <- pmatch("mean.mean", cnames)
+  }
+  if(is.na(mean.mtc)) stop("could not match 'mean' with column name of extractStats output")
+  
+  # risk along the efficient frontier
+  # get the match.col column
+  mtc <- pmatch(match.col, cnames)
+  if(is.na(mtc)) {
+    mtc <- pmatch(paste(match.col,match.col,sep='.'),cnames)
+  }
+  if(is.na(mtc)) stop("could not match match.col with column name of extractStats output")
+  
+  # compute the weights for the barplot
+  pos.weights <- +0.5 * (abs(wts) + wts)
+  neg.weights <- -0.5 * (abs(wts) - wts)
+  
+  # Define Plot Range:
+  ymax <- max(rowSums(pos.weights))
+  ymin <- min(rowSums(neg.weights))
+  range <- ymax - ymin
+  ymax <- ymax + 0.005 * range
+  ymin <- ymin - 0.005 * range
+  dim <- dim(wts)
+  range <- dim[1]
+  xmin <- 0
+  if(is.null(legend.loc)){
+    xmax <- range
+  } else {
+    xmax <- range + 0.3 * range
+  }
+  
+  # set the colorset if no colorset is passed in
+  if(is.null(colorset))
+    colorset <- 1:dim[2]
+  
+  # plot the positive weights
+  barplot(t(pos.weights), col = colorset, space = 0, ylab = "",
+          xlim = c(xmin, xmax), ylim = c(ymin, ymax),
+          border = element.color, cex.axis=cex.axis, 
+          axisnames=FALSE, ...)
+  
+  if(!is.null(legend.loc)){
+    if(legend.loc %in% c("topright", "right", "bottomright")){
+      # set the legend information
+      if(is.null(legend.labels)){
+        if(by.groups){
+          legend.labels <- names(groups)
+          if(is.null(legend.labels)) legend.labels <- constraints$group_labels
+        } else {
+          legend.labels <- gsub(pattern="^w\\.", replacement="", cnames[wts_idx])
+        }
+      }
+      legend(legend.loc, legend = legend.labels, bty = "n", cex = cex.legend, fill = colorset)
+    }
+  }
+  # plot the negative weights
+  barplot(t(neg.weights), col = colorset, space = 0, add = TRUE, border = element.color, 
+          cex.axis=cex.axis, axes=FALSE, axisnames=FALSE, ...)
+  
+  
+  # Add labels
+  ef.return <- frontier[, mean.mtc]
+  ef.risk <- frontier[, mtc]
+  n.risk <- length(ef.risk)
+  n.labels <- 6
+  M <- c(0, ( 1:(n.risk %/% n.labels) ) ) * n.labels + 1
+  # use 3 significant digits
+  axis(3, at = M, labels = signif(ef.risk[M], 3), cex.axis=cex.axis)
+  axis(1, at = M, labels = signif(ef.return[M], 3), cex.axis=cex.axis)
+  
+  # axis labels and titles
+  mtext(match.col, side = 3, line = 2, adj = 0.5, cex = cex.lab)
+  mtext("Mean", side = 1, line = 2, adj = 0.5, cex = cex.lab)
+  mtext("Weight", side = 2, line = 2, adj = 0.5, cex = cex.lab)
+  # add title
+  title(main=main, line=3)
+  # mtext(main, adj = 0, line = 2.5, font = 2, cex = 0.8)
+  box(col=element.color)
+}
+
+#' @rdname chart.Weights.EF
+#' @method chart.Weights.EF optimize.portfolio
+#' @S3method chart.Weights.EF optimize.portfolio
+chart.Weights.EF.optimize.portfolio <- function(object, ..., colorset=NULL, n.portfolios=25, by.groups=FALSE, match.col="ES", main="", cex.lab=0.8, cex.axis=0.8, cex.legend=0.8, legend.labels=NULL, element.color="darkgray", legend.loc="topright"){
+  # chart the weights along the efficient frontier of an objected created by optimize.portfolio
+  
+  if(!inherits(object, "optimize.portfolio")) stop("object must be of class optimize.portfolio")
+  
+  frontier <- extractEfficientFrontier(object=object, match.col=match.col, n.portfolios=n.portfolios)
+  PortfolioAnalytics:::chart.Weights.EF(object=frontier, colorset=colorset, ..., 
+                                        match.col=match.col, by.groups=by.groups, main=main, cex.lab=cex.lab, 
+                                        cex.axis=cex.axis, cex.legend=cex.legend, 
+                                        legend.labels=legend.labels, element.color=element.color,
+                                        legend.loc=legend.loc)
+}
+
+#' @rdname chart.EfficientFrontier
+#' @method chart.EfficientFrontier efficient.frontier
+#' @S3method chart.EfficientFrontier efficient.frontier
+chart.EfficientFrontier.efficient.frontier <- function(object, ..., match.col="ES", n.portfolios=NULL, xlim=NULL, ylim=NULL, cex.axis=0.8, element.color="darkgray", main="Efficient Frontier", RAR.text="SR", rf=0, tangent.line=TRUE, cex.legend=0.8, chart.assets=TRUE, labels.assets=TRUE, pch.assets=21, cex.assets=0.8){
+  if(!inherits(object, "efficient.frontier")) stop("object must be of class 'efficient.frontier'")
+  
+  # get the returns and efficient frontier object
+  R <- object$R
+  frontier <- object$frontier
+  
+  # get the column names from the frontier object
+  cnames <- colnames(frontier)
+  
+  # get the "mean" column
+  mean.mtc <- pmatch("mean", cnames)
+  if(is.na(mean.mtc)) {
+    mean.mtc <- pmatch("mean.mean", cnames)
+  }
+  if(is.na(mean.mtc)) stop("could not match 'mean' with column name of efficient frontier")
+  
+  # get the match.col column
+  mtc <- pmatch(match.col, cnames)
+  if(is.na(mtc)) {
+    mtc <- pmatch(paste(match.col,match.col,sep='.'),cnames)
+  }
+  if(is.na(mtc)) stop("could not match match.col with column name of efficient frontier")
+  
+  # get the data to plot scatter of asset returns
+  asset_ret <- scatterFUN(R=R, FUN="mean")
+  asset_risk <- scatterFUN(R=R, FUN=match.col)
+  rnames <- colnames(R)
+  
+  # set the x and y limits
+  if(is.null(xlim)){
+    xlim <- range(c(frontier[, mtc], asset_risk))
+    # xlim[1] <- xlim[1] * 0.8
+    xlim[1] <- 0
+    xlim[2] <- xlim[2] * 1.15
+  }
+  if(is.null(ylim)){
+    ylim <- range(c(frontier[, mean.mtc], asset_ret))
+    # ylim[1] <- ylim[1] * 0.9
+    ylim[1] <- 0
+    ylim[2] <- ylim[2] * 1.1
+  }
+  
+  if(!is.null(rf)){
+    sr <- (frontier[, mean.mtc] - rf) / (frontier[, mtc])
+    idx.maxsr <- which.max(sr)
+    srmax <- sr[idx.maxsr]
+  }
+  
+  # plot the efficient frontier line
+  plot(x=frontier[, mtc], y=frontier[, mean.mtc], ylab="Mean", xlab=match.col, main=main, xlim=xlim, ylim=ylim, axes=FALSE, ...)
+  
+  # Add the global minimum variance or global minimum ETL portfolio
+  points(x=frontier[1, mtc], y=frontier[1, mean.mtc], pch=16)
+  
+  if(chart.assets){
+    # risk-return scatter of the assets
+    points(x=asset_risk, y=asset_ret, pch=pch.assets, cex=cex.assets)
+    if(labels.assets) text(x=asset_risk, y=asset_ret, labels=rnames, pos=4, cex=cex.assets)
+  }
+  
+  if(!is.null(rf)){
+    # Plot tangency line and points at risk-free rate and tangency portfolio
+    if(tangent.line) abline(rf, srmax, lty=2)
+    points(0, rf, pch=16)
+    points(frontier[idx.maxsr, mtc], frontier[idx.maxsr, mean.mtc], pch=16)
+    # text(x=frontier[idx.maxsr], y=frontier[idx.maxsr], labels="T", pos=4, cex=0.8)
+    # Add legend with max Risk adjusted Return ratio and risk-free rate
+    legend("topleft", paste(RAR.text, " = ", signif(srmax,3), sep = ""), bty = "n", cex=cex.legend)
+    legend("topleft", inset = c(0,0.05), paste("rf = ", signif(rf,3), sep = ""), bty = "n", cex=cex.legend)
+  }
+  axis(1, cex.axis = cex.axis, col = element.color)
+  axis(2, cex.axis = cex.axis, col = element.color)
+  box(col = element.color)
+}
+
+#' Plot multiple efficient frontiers
+#' 
+#' Overlay the efficient frontiers of multiple portfolio objects on a single plot.
+#' 
+#' @param R an xts object of asset returns
+#' @param portfolio_list list of portfolio objects created by \code{\link{portfolio.spec}}
+#' @param type type of efficient frontier, see \code{\link{create.EfficientFrontier}}
+#' @param n.portfolios number of portfolios to extract along the efficient frontier.
+#' This is only used for objects of class \code{optimize.portfolio}
+#' @param match.col string name of column to use for risk (horizontal axis).
+#' Must match the name of an objective.
+#' @param search_size passed to optimize.portfolio for type="DEoptim" or type="random".
+#' @param main title used in the plot.
+#' @param cex.axis the magnification to be used for sizing the axis text relative to the current setting of 'cex', similar to \code{\link{plot}}.
+#' @param element.color provides the color for drawing less-important chart elements, such as the box lines, axis lines, etc.
+#' @param legend.loc location of the legend; NULL, "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center".
+#' @param legend.labels character vector to use for the legend labels.
+#' @param cex.legend The magnification to be used for sizing the legend relative to the current setting of 'cex', similar to \code{\link{plot}}.
+#' @param xlim set the x-axis limit, same as in \code{\link{plot}}.
+#' @param ylim set the y-axis limit, same as in \code{\link{plot}}.
+#' @param \dots passthrough parameters to \code{\link{plot}}.
+#' @param chart.assets TRUE/FALSE to include the assets.
+#' @param labels.assets TRUE/FALSE to include the asset names in the plot.
+#' @param pch.assets plotting character of the assets, same as in \code{\link{plot}}.
+#' @param cex.assets A numerical value giving the amount by which the asset points and labels should be magnified relative to the default.
+#' @param col vector of colors with length equal to the number of portfolios in \code{portfolio_list}.
+#' @param lty vector of line types with length equal to the number of portfolios in \code{portfolio_list}.
+#' @param lwd vector of line widths with length equal to the number of portfolios in \code{portfolio_list}.
+#' @author Ross Bennett
+#' @export
+chart.EfficientFrontierOverlay <- function(R, portfolio_list, type, n.portfolios=25, match.col="ES", search_size=2000, main="Efficient Frontiers", cex.axis=0.8, element.color="darkgray", legend.loc=NULL, legend.labels=NULL, cex.legend=0.8, xlim=NULL, ylim=NULL, ..., chart.assets=TRUE, labels.assets=TRUE, pch.assets=21, cex.assets=0.8, col=NULL, lty=NULL, lwd=NULL){
+  # create multiple efficient frontier objects (one per portfolio in portfolio_list)
+  if(!is.list(portfolio_list)) stop("portfolio_list must be passed in as a list")
+  if(length(portfolio_list) == 1) warning("Only one portfolio object in portfolio_list")
+  # store in out
+  out <- list()
+  for(i in 1:length(portfolio_list)){
+    if(!is.portfolio(portfolio_list[[i]])) stop("portfolio in portfolio_list must be of class 'portfolio'")
+    out[[i]] <- create.EfficientFrontier(R=R, portfolio=portfolio_list[[i]], type=type, n.portfolios=n.portfolios, match.col=match.col, search_size=search_size)
+  }
+  # get the data to plot scatter of asset returns
+  asset_ret <- scatterFUN(R=R, FUN="mean")
+  asset_risk <- scatterFUN(R=R, FUN=match.col)
+  rnames <- colnames(R)
+  
+  # set the x and y limits
+  if(is.null(xlim)){
+    xlim <- range(asset_risk)
+    # xlim[1] <- xlim[1] * 0.8
+    xlim[1] <- 0
+    xlim[2] <- xlim[2] * 1.15
+  }
+  if(is.null(ylim)){
+    ylim <- range(asset_ret)
+    # ylim[1] <- ylim[1] * 0.9
+    ylim[1] <- 0
+    ylim[2] <- ylim[2] * 1.1
+  }
+  
+  # plot the assets
+  plot(x=asset_risk, y=asset_ret, xlab=match.col, ylab="Mean", main=main, xlim=xlim, ylim=ylim, axes=FALSE, type="n", ...)
+  axis(1, cex.axis = cex.axis, col = element.color)
+  axis(2, cex.axis = cex.axis, col = element.color)
+  box(col = element.color)
+  
+  if(chart.assets){
+    # risk-return scatter of the assets
+    points(x=asset_risk, y=asset_ret, pch=pch.assets, cex=cex.assets)
+    if(labels.assets) text(x=asset_risk, y=asset_ret, labels=rnames, pos=4, cex=cex.assets)
+  }
+  
+  # set some basic plot parameters
+  if(is.null(col)) col <- 1:length(out)
+  if(is.null(lty)) lty <- 1:length(out)
+  if(is.null(lwd)) lwd <- rep(1, length(out))
+  
+  for(i in 1:length(out)){
+    tmp <- out[[i]]
+    tmpfrontier <- tmp$frontier
+    cnames <- colnames(tmpfrontier)
+    
+    # get the "mean" column
+    mean.mtc <- pmatch("mean", cnames)
+    if(is.na(mean.mtc)) {
+      mean.mtc <- pmatch("mean.mean", cnames)
+    }
+    if(is.na(mean.mtc)) stop("could not match 'mean' with column name of extractStats output")
+    
+    # get the match.col column
+    mtc <- pmatch(match.col, cnames)
+    if(is.na(mtc)) {
+      mtc <- pmatch(paste(match.col, match.col, sep='.'),cnames)
+    }
+    if(is.na(mtc)) stop("could not match match.col with column name of extractStats output")
+    # Add the efficient frontier lines to the plot
+    lines(x=tmpfrontier[, mtc], y=tmpfrontier[, mean.mtc], col=col[i], lty=lty[i], lwd=lwd[i])
+  }
+  if(!is.null(legend.loc)){
+    if(is.null(legend.labels)){
+      legend.labels <- paste("Portfolio", 1:length(out), sep=".")
+    }
+    legend(legend.loc, legend=legend.labels, col=col, lty=lty, lwd=lwd, cex=cex.legend, bty="n") 
+  }
+  return(invisible(out))
+}
+
+
+chart.Weights.GenSA <- function(object, ..., neighbors = NULL, main="Weights", las = 3, xlab=NULL, cex.lab = 1, element.color = "darkgray", cex.axis=0.8, colorset=NULL, legend.loc="topright", cex.legend=0.8, plot.type="line"){
+  
+  if(!inherits(object, "optimize.portfolio.GenSA")) stop("object must be of class 'optimize.portfolio.GenSA'")
+  
+  if(plot.type %in% c("bar", "barplot")){
+    barplotWeights(object=object, ..., main=main, las=las, xlab=xlab, cex.lab=cex.lab, element.color=element.color, cex.axis=cex.axis, legend.loc=legend.loc, cex.legend=cex.legend, colorset=colorset)
+  } else if(plot.type == "line"){
+    
+    columnnames = names(object$weights)
+    numassets = length(columnnames)
+    
+    constraints <- get_constraints(object$portfolio)
+    
+    if(is.null(xlab))
+      minmargin = 3
+    else
+      minmargin = 5
+    if(main=="") topmargin=1 else topmargin=4
+    if(las > 1) {# set the bottom border to accommodate labels
+      bottommargin = max(c(minmargin, (strwidth(columnnames,units="in"))/par("cin")[1])) * cex.lab
+      if(bottommargin > 10 ) {
+        bottommargin<-10
+        columnnames<-substr(columnnames,1,19)
+        # par(srt=45) #TODO figure out how to use text() and srt to rotate long labels
+      }
+    }
+    else {
+      bottommargin = minmargin
+    }
+    par(mar = c(bottommargin, 4, topmargin, 2) +.1)
+    if(any(is.infinite(constraints$max)) | any(is.infinite(constraints$min))){
+      # set ylim based on weights if box constraints contain Inf or -Inf
+      ylim <- range(object$weights)
+    } else {
+      # set ylim based on the range of box constraints min and max
+      ylim <- range(c(constraints$min, constraints$max))
+    }
+    plot(object$weights, type="b", col="blue", axes=FALSE, xlab='', ylim=ylim, ylab="Weights", main=main, pch=16, ...)
+    if(!any(is.infinite(constraints$min))){
+      points(constraints$min, type="b", col="darkgray", lty="solid", lwd=2, pch=24)
+    }
+    if(!any(is.infinite(constraints$max))){
+      points(constraints$max, type="b", col="darkgray", lty="solid", lwd=2, pch=25)
+    }
+    #     if(!is.null(neighbors)){ 
+    #         if(is.vector(neighbors)){
+    #             xtract=extractStats(ROI)
+    #             weightcols<-grep('w\\.',colnames(xtract)) #need \\. to get the dot 
+    #             if(length(neighbors)==1){
+    #                 # overplot nearby portfolios defined by 'out'
+    #                 orderx = order(xtract[,"out"])
+    #                 subsetx = head(xtract[orderx,], n=neighbors)
[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/returnanalytics -r 3302

From noreply at r-forge.r-project.org  Sun Jan 19 20:59:06 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Sun, 19 Jan 2014 20:59:06 +0100 (CET)
Subject: [Returnanalytics-commits] r3303 - pkg/PortfolioAnalytics/R
Message-ID: <20140119195906.392C9186C10@r-forge.r-project.org>

Author: braverock
Date: 2014-01-19 20:59:05 +0100 (Sun, 19 Jan 2014)
New Revision: 3303

Modified:
   pkg/PortfolioAnalytics/R/applyFUN.R
Log:
- roll back an errant `cat >>`


Modified: pkg/PortfolioAnalytics/R/applyFUN.R
===================================================================
--- pkg/PortfolioAnalytics/R/applyFUN.R	2014-01-19 19:52:42 UTC (rev 3302)
+++ pkg/PortfolioAnalytics/R/applyFUN.R	2014-01-19 19:59:05 UTC (rev 3303)
@@ -155,4107 +155,6 @@
   return(out)
 }
 
-#' classic risk reward scatter
-#' 
-#' This function charts the \code{optimize.portfolio} object in risk-return space.
-#' 
-#' @details
-#' \code{neighbors} may be specified in three ways.  
-#' The first is as a single number of neighbors.  This will extract the \code{neighbors} closest 
-#' portfolios in terms of the \code{out} numerical statistic.
-#' The second method consists of a numeric vector for \code{neighbors}.
-#' This will extract the \code{neighbors} with portfolio index numbers that correspond to the vector contents.
-#' The third method for specifying \code{neighbors} is to pass in a matrix.  
-#' This matrix should look like the output of \code{\link{extractStats}}, and should contain
-#' \code{risk.col},\code{return.col}, and weights columns all properly named. 
-#' 
-#' @param object optimal portfolio created by \code{\link{optimize.portfolio}}.
-#' @param neighbors set of 'neighbor' portfolios to overplot, see Details.
-#' @param \dots any other passthru parameters.
-#' @param return.col string matching the objective of a 'return' objective, on vertical axis.
-#' @param risk.col string matching the objective of a 'risk' objective, on horizontal axis.
-#' @param chart.assets TRUE/FALSE. Includes a risk reward scatter of the assets in the chart.
-#' @param element.color color for the default plot scatter points.
-#' @param cex.axis The magnification to be used for axis annotation relative to the current setting of \code{cex}.
-#' @param xlim set the x-axis limit, same as in \code{\link{plot}}.
-#' @param ylim set the y-axis limit, same as in \code{\link{plot}}.
-#' @param rp TRUE/FALSE to generate random portfolios to plot the feasible space
-#' @param main a main title for the plot.
-#' @param labels.assets TRUE/FALSE to include the names in the plot. 
-#' @param pch.assets plotting character of the assets, same as in \code{\link{plot}}
-#' @param cex.assets numerical value giving the amount by which the asset points should be magnified relative to the default.
-#' @param cex.lab numerical value giving the amount by which the labels should be magnified relative to the default.
-#' @param colorset color palette or vector of colors to use.
-#' @seealso \code{\link{optimize.portfolio}}
-#' @export
-chart.RiskReward <- function(object, ...){
-  UseMethod("chart.RiskReward")
-}
-
-
-#' Chart the efficient frontier and risk-return scatter
-#' 
-#' Chart the efficient frontier and risk-return scatter of the assets for 
-#' \code{optimize.portfolio} or \code{efficient.frontier} objects
-#' 
-#' @details
-#' For objects created by optimize.portfolio with 'DEoptim', 'random', or 'pso'
-#' specified as the optimize_method:
-#' \itemize{
-#'   \item The efficient frontier plotted is based on the the trace information (sets of 
-#'   portfolios tested by the solver at each iteration) in objects created by 
-#'   \code{optimize.portfolio}.
-#' }
-#' 
-#' For objects created by optimize.portfolio with 'ROI' specified as the 
-#' optimize_method:
-#' \itemize{
-#'   \item The mean-StdDev or mean-ETL efficient frontier can be plotted for optimal
-#'   portfolio objects created by \code{optimize.portfolio}.
-#' 
-#'   \item If \code{match.col="StdDev"}, the mean-StdDev efficient frontier is plotted.
-#' 
-#'   \item If \code{match.col="ETL"} (also "ES" or "CVaR"), the mean-ETL efficient frontier is plotted.
-#' }
-#' 
-#' Note that \code{trace=TRUE} must be specified in \code{\link{optimize.portfolio}}
-#' 
-#' GenSA does not return any useable trace information for portfolios tested at 
-#' each iteration, therfore we cannot extract and chart an efficient frontier.
-#' 
-#' By default, the tangency portfolio (maximum Sharpe Ratio or modified Sharpe Ratio)
-#' will be plotted using a risk free rate of 0. Set \code{rf=NULL} to omit 
-#' this from the plot. 
-#' 
-#' @param object object to chart.
-#' @param \dots passthru parameters to \code{\link{plot}}
-#' @param match.col string name of column to use for risk (horizontal axis).
-#' \code{match.col} must match the name of an objective measure in the 
-#' \code{objective_measures} or \code{opt_values} slot in the object created 
-#' by \code{\link{optimize.portfolio}}.
-#' @param n.portfolios number of portfolios to use to plot the efficient frontier.
-#' @param xlim set the x-axis limit, same as in \code{\link{plot}}.
-#' @param ylim set the y-axis limit, same as in \code{\link{plot}}.
-#' @param cex.axis numerical value giving the amount by which the axis should be magnified relative to the default.
-#' @param element.color provides the color for drawing less-important chart elements, such as the box lines, axis lines, etc.
-#' @param main a main title for the plot.
-#' @param RAR.text string name for risk adjusted return text to plot in the legend.
-#' @param rf risk free rate. If \code{rf} is not null, the maximum Sharpe Ratio or modified Sharpe Ratio tangency portfolio will be plotted.
-#' @param tangent.line TRUE/FALSE to plot the tangent line.
-#' @param cex.legend numerical value giving the amount by which the legend should be magnified relative to the default.
-#' @param chart.assets TRUE/FALSE to include the assets.
-#' @param labels.assets TRUE/FALSE to include the asset names in the plot. 
-#' \code{chart.assets} must be \code{TRUE} to plot asset names.
-#' @param pch.assets plotting character of the assets, same as in \code{\link{plot}}.
-#' @param cex.assets numerical value giving the amount by which the asset points and labels should be magnified relative to the default.
-#' @author Ross Bennett
-#' @rdname chart.EfficientFrontier
-#' @export
-chart.EfficientFrontier <- function(object, ...){
-  UseMethod("chart.EfficientFrontier")
-}
-
-#' @rdname chart.EfficientFrontier
-#' @method chart.EfficientFrontier optimize.portfolio.ROI
-#' @S3method chart.EfficientFrontier optimize.portfolio.ROI
-chart.EfficientFrontier.optimize.portfolio.ROI <- function(object, ..., match.col="ES", n.portfolios=25, xlim=NULL, ylim=NULL, cex.axis=0.8, element.color="darkgray", main="Efficient Frontier", RAR.text="SR", rf=0, tangent.line=TRUE, cex.legend=0.8, chart.assets=TRUE, labels.assets=TRUE, pch.assets=21, cex.assets=0.8){
-  if(!inherits(object, "optimize.portfolio.ROI")) stop("object must be of class optimize.portfolio.ROI")
-  
-  portf <- object$portfolio
-  R <- object$R
-  if(is.null(R)) stop(paste("Not able to get asset returns from", object))
-  wts <- object$weights
-  objectclass <- class(object)[1]
-  
-  # objnames <- unlist(lapply(portf$objectives, function(x) x$name))
-  # if(!(match.col %in% objnames)){
-  #   stop("match.col must match an objective name")
-  # }
-  
-  # get the optimal return and risk metrics
-  xtract <- extractStats(object=object)
-  columnames <- names(xtract)
-  if(!(("mean") %in% columnames)){
-    # we need to calculate the mean given the optimal weights
-    opt_ret <- applyFUN(R=R, weights=wts, FUN="mean")
-  } else {
-    opt_ret <- xtract["mean"]
-  }
-  # get the match.col column
-  mtc <- pmatch(match.col, columnames)
-  if(is.na(mtc)) {
-    mtc <- pmatch(paste(match.col,match.col,sep='.'), columnames)
-  }
-  if(is.na(mtc)){
-    # if(is.na(mtc)) stop("could not match match.col with column name of extractStats output")
-    opt_risk <- applyFUN(R=R, weights=wts, FUN=match.col)
-  } else {
-    opt_risk <- xtract[mtc]
-  }
-  
-  # get the data to plot scatter of asset returns
-  asset_ret <- scatterFUN(R=R, FUN="mean")
-  asset_risk <- scatterFUN(R=R, FUN=match.col)
-  rnames <- colnames(R)
-  
-  if(match.col %in% c("ETL", "ES", "CVaR")){
-    frontier <- meanetl.efficient.frontier(portfolio=portf, R=R, n.portfolios=n.portfolios)
-    rar <- "STARR"
-  }
-  if(match.col == "StdDev"){
-    frontier <- meanvar.efficient.frontier(portfolio=portf, R=R, n.portfolios=n.portfolios)
-    rar <- "SR"
-  }
-  # data points to plot the frontier
-  x.f <- frontier[, match.col]
-  y.f <- frontier[, "mean"]
-  
-  # Points for the Sharpe Ratio ((mu - rf) / StdDev) or STARR ((mu - rf) / ETL)
-  if(!is.null(rf)){
-    sr <- (y.f - rf) / (x.f)
-    idx.maxsr <- which.max(sr)
-    srmax <- sr[idx.maxsr]
-  }
-  
-  # set the x and y limits
-  if(is.null(xlim)){
-    xlim <- range(c(x.f, asset_risk))
-    # xlim[1] <- xlim[1] * 0.8
-    xlim[1] <- 0
-    xlim[2] <- xlim[2] * 1.15
-  }
-  if(is.null(ylim)){
-    ylim <- range(c(y.f, asset_ret))
-    # ylim[1] <- ylim[1] * 0.9
-    ylim[1] <- 0
-    ylim[2] <- ylim[2] * 1.1
-  }
-  
-  # plot the efficient frontier line
-  plot(x=x.f, y=y.f, ylab="Mean", xlab=match.col, main=main, xlim=xlim, ylim=ylim, axes=FALSE, ...)
-  
-  # Add the global minimum variance or global minimum ETL portfolio
-  points(x=x.f[1], y=y.f[1], pch=16)
-  
-  if(chart.assets){
-    # risk-return scatter of the assets
-    points(x=asset_risk, y=asset_ret, pch=pch.assets, cex=cex.assets)
-    if(labels.assets) text(x=asset_risk, y=asset_ret, labels=rnames, pos=4, cex=cex.assets)
-  }
-  
-  # plot the optimal portfolio
-  points(opt_risk, opt_ret, col="blue", pch=16) # optimal
-  text(x=opt_risk, y=opt_ret, labels="Optimal",col="blue", pos=4, cex=0.8)
-  if(!is.null(rf)){
-    # Plot tangency line and points at risk-free rate and tangency portfolio
-    if(tangent.line) abline(rf, srmax, lty=2)
-    points(0, rf, pch=16)
-    points(x.f[idx.maxsr], y.f[idx.maxsr], pch=16)
-    # text(x=x.f[idx.maxsr], y=y.f[idx.maxsr], labels="T", pos=4, cex=0.8)
-    # Add lengend with max Sharpe Ratio and risk-free rate
-    legend("topleft", paste(RAR.text, " = ", signif(srmax,3), sep = ""), bty = "n", cex=cex.legend)
-    legend("topleft", inset = c(0,0.05), paste("rf = ", signif(rf,3), sep = ""), bty = "n", cex=cex.legend)
-  }
-  axis(1, cex.axis = cex.axis, col = element.color)
-  axis(2, cex.axis = cex.axis, col = element.color)
-  box(col = element.color)
-}
-
-#' @rdname chart.EfficientFrontier
-#' @method chart.EfficientFrontier optimize.portfolio
-#' @S3method chart.EfficientFrontier optimize.portfolio
-chart.EfficientFrontier.optimize.portfolio <- function(object, ..., match.col="ES", n.portfolios=25, xlim=NULL, ylim=NULL, cex.axis=0.8, element.color="darkgray", main="Efficient Frontier", RAR.text="SR", rf=0, tangent.line=TRUE, cex.legend=0.8, chart.assets=TRUE, labels.assets=TRUE, pch.assets=21, cex.assets=0.8){
-  # This function will work with objects of class optimize.portfolio.DEoptim,
-  # optimize.portfolio.random, and optimize.portfolio.pso
-  
-  if(inherits(object, "optimize.portfolio.GenSA")){
-    stop("GenSA does not return any useable trace information for portfolios tested, thus we cannot extract an efficient frontier.")
-  }
-  
-  if(!inherits(object, "optimize.portfolio")) stop("object must be of class optimize.portfolio")
-  
-  portf <- object$portfolio
-  R <- object$R
-  if(is.null(R)) stop(paste("Not able to get asset returns from", object))
-  wts <- object$weights
-  
-  # get the stats from the object
-  xtract <- extractStats(object=object)
-  columnames <- colnames(xtract)
-  
-  # Check if match.col is in extractStats output
-  if(!(match.col %in% columnames)){
-    stop(paste(match.col, "is not a column in extractStats output"))
-  }
-  
-  # check if 'mean' is in extractStats output
-  if(!("mean" %in% columnames)){
-    stop("mean is not a column in extractStats output")
-  }
-  
-  # get the stats of the optimal portfolio
-  optstats <- xtract[which.min(xtract[, "out"]), ]
-  opt_ret <- optstats["mean"]
-  opt_risk <- optstats[match.col]
-  
-  # get the data to plot scatter of asset returns
-  asset_ret <- scatterFUN(R=R, FUN="mean")
-  asset_risk <- scatterFUN(R=R, FUN=match.col)
-  rnames <- colnames(R)
-  
-  # get the data of the efficient frontier
-  frontier <- extract.efficient.frontier(object=object, match.col=match.col, n.portfolios=n.portfolios)
-  
-  # data points to plot the frontier
-  x.f <- frontier[, match.col]
-  y.f <- frontier[, "mean"]
-  
-  # Points for the Sharpe or Modified Sharpe Ratio
-  if(!is.null(rf)){
-    sr <- (y.f - rf) / (x.f)
-    idx.maxsr <- which.max(sr)
-    srmax <- sr[idx.maxsr]
-  }
-  
-  # set the x and y limits
-  if(is.null(xlim)){
-    xlim <- range(c(x.f, asset_risk))
-    # xlim[1] <- xlim[1] * 0.8
-    xlim[1] <- 0
-    xlim[2] <- xlim[2] * 1.15
-  }
-  if(is.null(ylim)){
-    ylim <- range(c(y.f, asset_ret))
-    # ylim[1] <- ylim[1] * 0.9
-    ylim[1] <- 0
-    ylim[2] <- ylim[2] * 1.1
-  }
-  
-  # plot the efficient frontier line
-  plot(x=x.f, y=y.f, ylab="Mean", xlab=match.col, main=main, xlim=xlim, ylim=ylim, axes=FALSE, ...)
-  
-  # Add the global minimum variance or global minimum ETL portfolio
-  points(x=x.f[1], y=y.f[1], pch=16)
-  
-  if(chart.assets){
-    # risk-return scatter of the assets
-    points(x=asset_risk, y=asset_ret, pch=pch.assets, cex=cex.assets)
-    if(labels.assets) text(x=asset_risk, y=asset_ret, labels=rnames, pos=4, cex=cex.assets)
-  }
-  
-  # plot the optimal portfolio
-  points(opt_risk, opt_ret, col="blue", pch=16) # optimal
-  text(x=opt_risk, y=opt_ret, labels="Optimal",col="blue", pos=4, cex=0.8)
-  if(!is.null(rf)){
-    # Plot tangency line and points at risk-free rate and tangency portfolio
-    if(tangent.line) abline(rf, srmax, lty=2)
-    points(0, rf, pch=16)
-    points(x.f[idx.maxsr], y.f[idx.maxsr], pch=16)
-    # text(x=x.f[idx.maxsr], y=y.f[idx.maxsr], labels="T", pos=4, cex=0.8)
-    # Add lengend with max Sharpe Ratio and risk-free rate
-    legend("topleft", paste(RAR.text, " = ", signif(srmax,3), sep = ""), bty = "n", cex=cex.legend)
-    legend("topleft", inset = c(0,0.05), paste("rf = ", signif(rf,3), sep = ""), bty = "n", cex=cex.legend)
-  }
-  axis(1, cex.axis = cex.axis, col = element.color)
-  axis(2, cex.axis = cex.axis, col = element.color)
-  box(col = element.color)
-}
-
-
-#' Chart weights along an efficient frontier
-#' 
-#' This function produces a stacked barplot of weights along an efficient frontier.
-#' 
-#' @param object object of class \code{efficient.frontier} or \code{optimize.portfolio}.
-#' @param \dots passthru parameters to \code{barplot}.
-#' @param colorset color palette or vector of colors to use.
-#' @param n.portfolios number of portfolios to extract along the efficient frontier.
-#' @param by.groups TRUE/FALSE. If TRUE, the group weights are charted.
-#' @param match.col string name of column to use for risk (horizontal axis). Must match the name of an objective.
-#' @param main title used in the plot.
-#' @param cex.lab the magnification to be used for x-axis and y-axis labels relative to the current setting of 'cex'.
-#' @param cex.axis the magnification to be used for sizing the axis text relative to the current setting of 'cex', similar to \code{\link{plot}}.
-#' @param cex.legend the magnification to be used for sizing the legend relative to the current setting of 'cex', similar to \code{\link{plot}}.
-#' @param legend.labels character vector to use for the legend labels.
-#' @param element.color provides the color for drawing less-important chart elements, such as the box lines, axis lines, etc.
-#' @param legend.loc NULL, "topright", "right", or "bottomright". If legend.loc is NULL, the legend will not be plotted.
-#' @author Ross Bennett
-#' @rdname chart.Weights.EF
-#' @export
-chart.Weights.EF <- function(object, ...){
-  UseMethod("chart.Weights.EF")
-}
-
-
-#' @rdname chart.Weights.EF
-#' @method chart.Weights.EF efficient.frontier
-#' @S3method chart.Weights.EF efficient.frontier
-chart.Weights.EF.efficient.frontier <- function(object, ..., colorset=NULL, n.portfolios=25, by.groups=FALSE, match.col="ES", main="", cex.lab=0.8, cex.axis=0.8, cex.legend=0.8, legend.labels=NULL, element.color="darkgray", legend.loc="topright"){
-  # using ideas from weightsPlot.R in fPortfolio package
-  
-  if(!inherits(object, "efficient.frontier")) stop("object must be of class 'efficient.frontier'")
-  
-  if(is.list(object)){
-    # Objects created with create.EfficientFrontier will be a list of 2 elements
-    frontier <- object$frontier
-  } else {
-    # Objects created with extractEfficientFrontier will only be an efficient.frontier object
-    frontier <- object
-  }
-  
-  
-  # get the columns with weights
-  cnames <- colnames(frontier)
-  wts_idx <- grep(pattern="^w\\.", cnames)
-  wts <- frontier[, wts_idx]
-  
-  if(by.groups){
-    constraints <- get_constraints(object$portfolio)
-    groups <- constraints$groups
-    if(is.null(groups)) stop("group constraints not in portfolio object")
-    if(!is.null(groups)){
-      groupfun <- function(weights, groups){
-        # This function is to calculate weights by group given the group list
-        # and a matrix of weights along the efficient frontier
-        ngroups <- length(groups)
-        group_weights <- rep(0, ngroups)
-        for(i in 1:ngroups){
-          group_weights[i] <- sum(weights[groups[[i]]])
-        }
-        group_weights
-      }
-      wts <- t(apply(wts, 1, groupfun, groups=groups))
-    }
-  }
-  
-  # return along the efficient frontier
-  # get the "mean" column
-  mean.mtc <- pmatch("mean", cnames)
-  if(is.na(mean.mtc)) {
-    mean.mtc <- pmatch("mean.mean", cnames)
-  }
-  if(is.na(mean.mtc)) stop("could not match 'mean' with column name of extractStats output")
-  
-  # risk along the efficient frontier
-  # get the match.col column
-  mtc <- pmatch(match.col, cnames)
-  if(is.na(mtc)) {
-    mtc <- pmatch(paste(match.col,match.col,sep='.'),cnames)
-  }
-  if(is.na(mtc)) stop("could not match match.col with column name of extractStats output")
-  
-  # compute the weights for the barplot
-  pos.weights <- +0.5 * (abs(wts) + wts)
-  neg.weights <- -0.5 * (abs(wts) - wts)
-  
-  # Define Plot Range:
-  ymax <- max(rowSums(pos.weights))
-  ymin <- min(rowSums(neg.weights))
-  range <- ymax - ymin
-  ymax <- ymax + 0.005 * range
-  ymin <- ymin - 0.005 * range
-  dim <- dim(wts)
-  range <- dim[1]
-  xmin <- 0
-  if(is.null(legend.loc)){
-    xmax <- range
-  } else {
-    xmax <- range + 0.3 * range
-  }
-  
-  # set the colorset if no colorset is passed in
-  if(is.null(colorset))
-    colorset <- 1:dim[2]
-  
-  # plot the positive weights
-  barplot(t(pos.weights), col = colorset, space = 0, ylab = "",
-          xlim = c(xmin, xmax), ylim = c(ymin, ymax),
-          border = element.color, cex.axis=cex.axis, 
-          axisnames=FALSE, ...)
-  
-  if(!is.null(legend.loc)){
-    if(legend.loc %in% c("topright", "right", "bottomright")){
-      # set the legend information
-      if(is.null(legend.labels)){
-        if(by.groups){
-          legend.labels <- names(groups)
-          if(is.null(legend.labels)) legend.labels <- constraints$group_labels
-        } else {
-          legend.labels <- gsub(pattern="^w\\.", replacement="", cnames[wts_idx])
-        }
-      }
-      legend(legend.loc, legend = legend.labels, bty = "n", cex = cex.legend, fill = colorset)
-    }
-  }
-  # plot the negative weights
-  barplot(t(neg.weights), col = colorset, space = 0, add = TRUE, border = element.color, 
-          cex.axis=cex.axis, axes=FALSE, axisnames=FALSE, ...)
-  
-  
-  # Add labels
-  ef.return <- frontier[, mean.mtc]
-  ef.risk <- frontier[, mtc]
-  n.risk <- length(ef.risk)
-  n.labels <- 6
-  M <- c(0, ( 1:(n.risk %/% n.labels) ) ) * n.labels + 1
-  # use 3 significant digits
-  axis(3, at = M, labels = signif(ef.risk[M], 3), cex.axis=cex.axis)
-  axis(1, at = M, labels = signif(ef.return[M], 3), cex.axis=cex.axis)
-  
-  # axis labels and titles
-  mtext(match.col, side = 3, line = 2, adj = 0.5, cex = cex.lab)
-  mtext("Mean", side = 1, line = 2, adj = 0.5, cex = cex.lab)
-  mtext("Weight", side = 2, line = 2, adj = 0.5, cex = cex.lab)
-  # add title
-  title(main=main, line=3)
-  # mtext(main, adj = 0, line = 2.5, font = 2, cex = 0.8)
-  box(col=element.color)
-}
-
-#' @rdname chart.Weights.EF
-#' @method chart.Weights.EF optimize.portfolio
-#' @S3method chart.Weights.EF optimize.portfolio
-chart.Weights.EF.optimize.portfolio <- function(object, ..., colorset=NULL, n.portfolios=25, by.groups=FALSE, match.col="ES", main="", cex.lab=0.8, cex.axis=0.8, cex.legend=0.8, legend.labels=NULL, element.color="darkgray", legend.loc="topright"){
-  # chart the weights along the efficient frontier of an objected created by optimize.portfolio
-  
-  if(!inherits(object, "optimize.portfolio")) stop("object must be of class optimize.portfolio")
-  
-  frontier <- extractEfficientFrontier(object=object, match.col=match.col, n.portfolios=n.portfolios)
-  PortfolioAnalytics:::chart.Weights.EF(object=frontier, colorset=colorset, ..., 
-                                        match.col=match.col, by.groups=by.groups, main=main, cex.lab=cex.lab, 
-                                        cex.axis=cex.axis, cex.legend=cex.legend, 
-                                        legend.labels=legend.labels, element.color=element.color,
-                                        legend.loc=legend.loc)
-}
-
-#' @rdname chart.EfficientFrontier
-#' @method chart.EfficientFrontier efficient.frontier
-#' @S3method chart.EfficientFrontier efficient.frontier
-chart.EfficientFrontier.efficient.frontier <- function(object, ..., match.col="ES", n.portfolios=NULL, xlim=NULL, ylim=NULL, cex.axis=0.8, element.color="darkgray", main="Efficient Frontier", RAR.text="SR", rf=0, tangent.line=TRUE, cex.legend=0.8, chart.assets=TRUE, labels.assets=TRUE, pch.assets=21, cex.assets=0.8){
-  if(!inherits(object, "efficient.frontier")) stop("object must be of class 'efficient.frontier'")
-  
-  # get the returns and efficient frontier object
-  R <- object$R
-  frontier <- object$frontier
-  
-  # get the column names from the frontier object
-  cnames <- colnames(frontier)
-  
-  # get the "mean" column
-  mean.mtc <- pmatch("mean", cnames)
-  if(is.na(mean.mtc)) {
-    mean.mtc <- pmatch("mean.mean", cnames)
-  }
-  if(is.na(mean.mtc)) stop("could not match 'mean' with column name of efficient frontier")
-  
-  # get the match.col column
-  mtc <- pmatch(match.col, cnames)
-  if(is.na(mtc)) {
-    mtc <- pmatch(paste(match.col,match.col,sep='.'),cnames)
-  }
-  if(is.na(mtc)) stop("could not match match.col with column name of efficient frontier")
-  
-  # get the data to plot scatter of asset returns
-  asset_ret <- scatterFUN(R=R, FUN="mean")
-  asset_risk <- scatterFUN(R=R, FUN=match.col)
-  rnames <- colnames(R)
-  
-  # set the x and y limits
-  if(is.null(xlim)){
-    xlim <- range(c(frontier[, mtc], asset_risk))
-    # xlim[1] <- xlim[1] * 0.8
-    xlim[1] <- 0
-    xlim[2] <- xlim[2] * 1.15
-  }
-  if(is.null(ylim)){
-    ylim <- range(c(frontier[, mean.mtc], asset_ret))
-    # ylim[1] <- ylim[1] * 0.9
-    ylim[1] <- 0
-    ylim[2] <- ylim[2] * 1.1
-  }
-  
-  if(!is.null(rf)){
-    sr <- (frontier[, mean.mtc] - rf) / (frontier[, mtc])
-    idx.maxsr <- which.max(sr)
-    srmax <- sr[idx.maxsr]
-  }
-  
-  # plot the efficient frontier line
-  plot(x=frontier[, mtc], y=frontier[, mean.mtc], ylab="Mean", xlab=match.col, main=main, xlim=xlim, ylim=ylim, axes=FALSE, ...)
-  
-  # Add the global minimum variance or global minimum ETL portfolio
-  points(x=frontier[1, mtc], y=frontier[1, mean.mtc], pch=16)
-  
-  if(chart.assets){
-    # risk-return scatter of the assets
-    points(x=asset_risk, y=asset_ret, pch=pch.assets, cex=cex.assets)
-    if(labels.assets) text(x=asset_risk, y=asset_ret, labels=rnames, pos=4, cex=cex.assets)
-  }
-  
-  if(!is.null(rf)){
-    # Plot tangency line and points at risk-free rate and tangency portfolio
-    if(tangent.line) abline(rf, srmax, lty=2)
-    points(0, rf, pch=16)
-    points(frontier[idx.maxsr, mtc], frontier[idx.maxsr, mean.mtc], pch=16)
-    # text(x=frontier[idx.maxsr], y=frontier[idx.maxsr], labels="T", pos=4, cex=0.8)
-    # Add legend with max Risk adjusted Return ratio and risk-free rate
-    legend("topleft", paste(RAR.text, " = ", signif(srmax,3), sep = ""), bty = "n", cex=cex.legend)
-    legend("topleft", inset = c(0,0.05), paste("rf = ", signif(rf,3), sep = ""), bty = "n", cex=cex.legend)
-  }
-  axis(1, cex.axis = cex.axis, col = element.color)
-  axis(2, cex.axis = cex.axis, col = element.color)
-  box(col = element.color)
-}
-
-#' Plot multiple efficient frontiers
-#' 
-#' Overlay the efficient frontiers of multiple portfolio objects on a single plot.
-#' 
-#' @param R an xts object of asset returns
-#' @param portfolio_list list of portfolio objects created by \code{\link{portfolio.spec}}
-#' @param type type of efficient frontier, see \code{\link{create.EfficientFrontier}}
-#' @param n.portfolios number of portfolios to extract along the efficient frontier.
-#' This is only used for objects of class \code{optimize.portfolio}
-#' @param match.col string name of column to use for risk (horizontal axis).
-#' Must match the name of an objective.
-#' @param search_size passed to optimize.portfolio for type="DEoptim" or type="random".
-#' @param main title used in the plot.
-#' @param cex.axis the magnification to be used for sizing the axis text relative to the current setting of 'cex', similar to \code{\link{plot}}.
-#' @param element.color provides the color for drawing less-important chart elements, such as the box lines, axis lines, etc.
-#' @param legend.loc location of the legend; NULL, "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center".
-#' @param legend.labels character vector to use for the legend labels.
-#' @param cex.legend The magnification to be used for sizing the legend relative to the current setting of 'cex', similar to \code{\link{plot}}.
-#' @param xlim set the x-axis limit, same as in \code{\link{plot}}.
-#' @param ylim set the y-axis limit, same as in \code{\link{plot}}.
-#' @param \dots passthrough parameters to \code{\link{plot}}.
-#' @param chart.assets TRUE/FALSE to include the assets.
-#' @param labels.assets TRUE/FALSE to include the asset names in the plot.
-#' @param pch.assets plotting character of the assets, same as in \code{\link{plot}}.
-#' @param cex.assets A numerical value giving the amount by which the asset points and labels should be magnified relative to the default.
-#' @param col vector of colors with length equal to the number of portfolios in \code{portfolio_list}.
-#' @param lty vector of line types with length equal to the number of portfolios in \code{portfolio_list}.
-#' @param lwd vector of line widths with length equal to the number of portfolios in \code{portfolio_list}.
-#' @author Ross Bennett
-#' @export
-chart.EfficientFrontierOverlay <- function(R, portfolio_list, type, n.portfolios=25, match.col="ES", search_size=2000, main="Efficient Frontiers", cex.axis=0.8, element.color="darkgray", legend.loc=NULL, legend.labels=NULL, cex.legend=0.8, xlim=NULL, ylim=NULL, ..., chart.assets=TRUE, labels.assets=TRUE, pch.assets=21, cex.assets=0.8, col=NULL, lty=NULL, lwd=NULL){
-  # create multiple efficient frontier objects (one per portfolio in portfolio_list)
-  if(!is.list(portfolio_list)) stop("portfolio_list must be passed in as a list")
-  if(length(portfolio_list) == 1) warning("Only one portfolio object in portfolio_list")
-  # store in out
-  out <- list()
-  for(i in 1:length(portfolio_list)){
-    if(!is.portfolio(portfolio_list[[i]])) stop("portfolio in portfolio_list must be of class 'portfolio'")
-    out[[i]] <- create.EfficientFrontier(R=R, portfolio=portfolio_list[[i]], type=type, n.portfolios=n.portfolios, match.col=match.col, search_size=search_size)
-  }
-  # get the data to plot scatter of asset returns
-  asset_ret <- scatterFUN(R=R, FUN="mean")
-  asset_risk <- scatterFUN(R=R, FUN=match.col)
-  rnames <- colnames(R)
-  
-  # set the x and y limits
-  if(is.null(xlim)){
-    xlim <- range(asset_risk)
-    # xlim[1] <- xlim[1] * 0.8
-    xlim[1] <- 0
-    xlim[2] <- xlim[2] * 1.15
-  }
-  if(is.null(ylim)){
-    ylim <- range(asset_ret)
-    # ylim[1] <- ylim[1] * 0.9
-    ylim[1] <- 0
-    ylim[2] <- ylim[2] * 1.1
-  }
-  
-  # plot the assets
-  plot(x=asset_risk, y=asset_ret, xlab=match.col, ylab="Mean", main=main, xlim=xlim, ylim=ylim, axes=FALSE, type="n", ...)
-  axis(1, cex.axis = cex.axis, col = element.color)
-  axis(2, cex.axis = cex.axis, col = element.color)
-  box(col = element.color)
-  
-  if(chart.assets){
-    # risk-return scatter of the assets
-    points(x=asset_risk, y=asset_ret, pch=pch.assets, cex=cex.assets)
-    if(labels.assets) text(x=asset_risk, y=asset_ret, labels=rnames, pos=4, cex=cex.assets)
-  }
-  
-  # set some basic plot parameters
-  if(is.null(col)) col <- 1:length(out)
-  if(is.null(lty)) lty <- 1:length(out)
-  if(is.null(lwd)) lwd <- rep(1, length(out))
-  
-  for(i in 1:length(out)){
-    tmp <- out[[i]]
-    tmpfrontier <- tmp$frontier
-    cnames <- colnames(tmpfrontier)
-    
-    # get the "mean" column
-    mean.mtc <- pmatch("mean", cnames)
-    if(is.na(mean.mtc)) {
-      mean.mtc <- pmatch("mean.mean", cnames)
-    }
-    if(is.na(mean.mtc)) stop("could not match 'mean' with column name of extractStats output")
-    
-    # get the match.col column
-    mtc <- pmatch(match.col, cnames)
-    if(is.na(mtc)) {
-      mtc <- pmatch(paste(match.col, match.col, sep='.'),cnames)
-    }
-    if(is.na(mtc)) stop("could not match match.col with column name of extractStats output")
-    # Add the efficient frontier lines to the plot
-    lines(x=tmpfrontier[, mtc], y=tmpfrontier[, mean.mtc], col=col[i], lty=lty[i], lwd=lwd[i])
-  }
-  if(!is.null(legend.loc)){
-    if(is.null(legend.labels)){
-      legend.labels <- paste("Portfolio", 1:length(out), sep=".")
-    }
-    legend(legend.loc, legend=legend.labels, col=col, lty=lty, lwd=lwd, cex=cex.legend, bty="n") 
-  }
-  return(invisible(out))
-}
-
-
-chart.Weights.GenSA <- function(object, ..., neighbors = NULL, main="Weights", las = 3, xlab=NULL, cex.lab = 1, element.color = "darkgray", cex.axis=0.8, colorset=NULL, legend.loc="topright", cex.legend=0.8, plot.type="line"){
-  
-  if(!inherits(object, "optimize.portfolio.GenSA")) stop("object must be of class 'optimize.portfolio.GenSA'")
-  
-  if(plot.type %in% c("bar", "barplot")){
-    barplotWeights(object=object, ..., main=main, las=las, xlab=xlab, cex.lab=cex.lab, element.color=element.color, cex.axis=cex.axis, legend.loc=legend.loc, cex.legend=cex.legend, colorset=colorset)
-  } else if(plot.type == "line"){
-    
-    columnnames = names(object$weights)
-    numassets = length(columnnames)
-    
-    constraints <- get_constraints(object$portfolio)
-    
-    if(is.null(xlab))
-      minmargin = 3
-    else
-      minmargin = 5
-    if(main=="") topmargin=1 else topmargin=4
-    if(las > 1) {# set the bottom border to accommodate labels
-      bottommargin = max(c(minmargin, (strwidth(columnnames,units="in"))/par("cin")[1])) * cex.lab
-      if(bottommargin > 10 ) {
-        bottommargin<-10
-        columnnames<-substr(columnnames,1,19)
-        # par(srt=45) #TODO figure out how to use text() and srt to rotate long labels
-      }
-    }
-    else {
-      bottommargin = minmargin
-    }
-    par(mar = c(bottommargin, 4, topmargin, 2) +.1)
-    if(any(is.infinite(constraints$max)) | any(is.infinite(constraints$min))){
-      # set ylim based on weights if box constraints contain Inf or -Inf
-      ylim <- range(object$weights)
-    } else {
-      # set ylim based on the range of box constraints min and max
-      ylim <- range(c(constraints$min, constraints$max))
-    }
-    plot(object$weights, type="b", col="blue", axes=FALSE, xlab='', ylim=ylim, ylab="Weights", main=main, pch=16, ...)
-    if(!any(is.infinite(constraints$min))){
-      points(constraints$min, type="b", col="darkgray", lty="solid", lwd=2, pch=24)
-    }
-    if(!any(is.infinite(constraints$max))){
-      points(constraints$max, type="b", col="darkgray", lty="solid", lwd=2, pch=25)
-    }
-    #     if(!is.null(neighbors)){ 
-    #         if(is.vector(neighbors)){
-    #             xtract=extractStats(ROI)
-    #             weightcols<-grep('w\\.',colnames(xtract)) #need \\. to get the dot 
-    #             if(length(neighbors)==1){
-    #                 # overplot nearby portfolios defined by 'out'
-    #                 orderx = order(xtract[,"out"])
-    #                 subsetx = head(xtract[orderx,], n=neighbors)
-    #                 for(i in 1:neighbors) points(subsetx[i,weightcols], type="b", col="lightblue")
-    #             } else{
-    #                 # assume we have a vector of portfolio numbers
-    #                 subsetx = xtract[neighbors,weightcols]
-    #                 for(i in 1:length(neighbors)) points(subsetx[i,], type="b", col="lightblue")
-    #             }      
-    #         }
-    #         if(is.matrix(neighbors) | is.data.frame(neighbors)){
-    #             # the user has likely passed in a matrix containing calculated values for risk.col and return.col
-    #             nbweights<-grep('w\\.',colnames(neighbors)) #need \\. to get the dot
-    #             for(i in 1:nrow(neighbors)) points(as.numeric(neighbors[i,nbweights]), type="b", col="lightblue")
-    #             # note that here we need to get weight cols separately from the matrix, not from xtract
[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/returnanalytics -r 3303

From noreply at r-forge.r-project.org  Sun Jan 19 21:09:15 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Sun, 19 Jan 2014 21:09:15 +0100 (CET)
Subject: [Returnanalytics-commits] r3304 - pkg/PortfolioAnalytics
Message-ID: <20140119200915.2A2E41810EC@r-forge.r-project.org>

Author: braverock
Date: 2014-01-19 21:09:14 +0100 (Sun, 19 Jan 2014)
New Revision: 3304

Added:
   pkg/PortfolioAnalytics/codeblock.txt
Modified:
   pkg/PortfolioAnalytics/.Rbuildignore
Log:
- add Copyright template
- update .Rbuildignore


Modified: pkg/PortfolioAnalytics/.Rbuildignore
===================================================================
--- pkg/PortfolioAnalytics/.Rbuildignore	2014-01-19 19:59:05 UTC (rev 3303)
+++ pkg/PortfolioAnalytics/.Rbuildignore	2014-01-19 20:09:14 UTC (rev 3304)
@@ -3,3 +3,4 @@
 .svn
 ^\.Rproj\.user$
 ^.*\.Rproj$
+codeblock.txt

Added: pkg/PortfolioAnalytics/codeblock.txt
===================================================================
--- pkg/PortfolioAnalytics/codeblock.txt	                        (rev 0)
+++ pkg/PortfolioAnalytics/codeblock.txt	2014-01-19 20:09:14 UTC (rev 3304)
@@ -0,0 +1,12 @@
+
+###############################################################################
+# R (http://r-project.org/) Numeric Methods for Optimization of Portfolios
+#
+# Copyright (c) 2004-2014 Brian G. Peterson, Peter Carl, Ross Bennett, Kris Boudt
+#
+# This library is distributed under the terms of the GNU Public License (GPL)
+# for full details see the file COPYING
+#
+# $Id: applyFUN.R 3302 2014-01-19 19:52:42Z braverock $
+#
+###############################################################################


From noreply at r-forge.r-project.org  Wed Jan 29 00:30:51 2014
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Wed, 29 Jan 2014 00:30:51 +0100 (CET)
Subject: [Returnanalytics-commits] r3305 - pkg/PerformanceAnalytics/R
Message-ID: <20140128233051.518081846C6@r-forge.r-project.org>

Author: peter_carl
Date: 2014-01-29 00:30:50 +0100 (Wed, 29 Jan 2014)
New Revision: 3305

Modified:
   pkg/PerformanceAnalytics/R/Omega.R
Log:
- fixed Hmisc reference for CRAN note 


Modified: pkg/PerformanceAnalytics/R/Omega.R
===================================================================
--- pkg/PerformanceAnalytics/R/Omega.R	2014-01-19 20:09:14 UTC (rev 3304)
+++ pkg/PerformanceAnalytics/R/Omega.R	2014-01-28 23:30:50 UTC (rev 3305)
@@ -143,7 +143,7 @@
                 a = min(x)
                 b = max(x)
 
-                xcdf = Hmisc:::Ecdf.default(x, pl=FALSE)
+                xcdf = Hmisc::Ecdf.default(x, pl=FALSE)
                 f <- approxfun(xcdf$x,xcdf$y,method="linear",ties="ordered")
 
                 if(output == "full") {