Fixing small details before CRAN submission.

DESCRIPTION

@@ -48,11 +48,7 @@ Remotes:
     google/patrick
 Suggests:
     testthat,
-    patrick,
-    knitr,
-    rmarkdown,
-    bookdown,
-    prettydoc
+    patrick
 VignetteBuilder:
     knitr,
     rmarkdown,

R/riskParityPortfolio.R

@@ -526,8 +526,10 @@ riskParityPortfolioCyclicalSpinu <- function(Sigma, b = rep(1/nrow(Sigma), nrow(
 #' comparison, with several illustrative examples.
 #'
 #' @param Sigma covariance or correlation matrix
-#' @param b budget vector, aka, risk budgeting targets
-#' @param mu vector of expected returns
+#' @param b budget vector, aka risk budgeting targets. The default is the uniform
+#'        1/N vector.
+#' @param mu vector of expected returns (only needed if the expected return term 
+#'        is desired in the objective)
 #' @param lmd_mu scalar that controls the importance of the expected return term
 #' @param lmd_var scalar that controls the importance of the variance term
 #'        (only available for the SCA method for now).
@@ -538,47 +540,47 @@ riskParityPortfolioCyclicalSpinu <- function(Sigma, b = rep(1/nrow(Sigma), nrow(
 #'        then the upper bound is applied element-wise
 #'        (only available for the SCA method for now).
 #' @param method_init which algorithm to use for computing the initial portfolio
-#'        solution. We recommend choosing "cyclical" for high-dimensional
-#'        (N > 500) portfolios since it scales better in that regime
-#' @param method which optimization method to use.
+#'        solution. We recommend choosing cyclical over Newton for high-dimensional
+#'        (N > 500) portfolios since it scales better in that regime. The 
+#'        default is \code{"cyclical-spinu"}.
+#' @param method which optimization method to use. The default is "sca".
 #' @param formulation string indicating the formulation to be used for the risk
-#'        parity optimization problem. It must be one of: "diag", "rc-double-index",
+#'        parity optimization problem. It must be one of: \code{"diag", "rc-double-index",
 #'        "rc-over-b-double-index", "rc-over-var vs b", "rc-over-var",
 #'        "rc-over-sd vs b-times-sd", "rc vs b-times-var", "rc vs theta", or
-#'        "rc-over-b vs theta". If formulation is NULL and no additional terms
+#'        "rc-over-b vs theta"}. If \code{formulation} is \code{NULL} and no additional terms
 #'        or constraints are set, such as expected return or shortselling, then
 #'        the vanilla risk parity portfolio will be returned. If formulation is
-#'        "diag" then the analytical solution of the risk parity optimization for
+#'        \code{"diag"} then the analytical solution of the risk parity optimization for
 #'        for a diagonal covariance matrix will be returned.
 #' @param w0 initial value for the portfolio weights. Default is the vanilla
-#'        portfolio computed either with Newton or Cyclical methods.
-#' @param theta0 initial value for theta. If NULL, the optimum solution for a fixed
-#'        vector of portfolio weights will be used
-#' @param gamma learning rate
-#' @param zeta factor used to decrease the learning rate at each iteration
-#' @param tau regularization factor. If NULL, a meaningful value will be used
+#'        portfolio computed either with cyclical or Newton methods.
+#' @param theta0 initial value for theta (in case formulation uses theta). If \code{NULL}, 
+#'        the optimum solution for a fixed vector of portfolio weights will be used.
+#' @param gamma learning rate for the SCA method.
+#' @param zeta factor used to decrease the learning rate at each iteration for the SCA method.
+#' @param tau regularization factor. If \code{NULL}, a meaningful value will be used
 #' @param maxiter maximum number of iterations for the SCA loop
 #' @param ftol convergence tolerance on the risk contribution target
 #' @param wtol convergence tolerance on the values of the portfolio weights
 #' @param use_gradient (this parameter is meaningful only if method is either
-#'        "alabama" or "slsqp") if TRUE, gradients of the objective function wrt to the
-#'        parameters will be used. This is strongly recommended to achieve faster
-#'        results.
+#'        \code{"alabama"} or \code{"slsqp"}) if \code{TRUE}, gradients of the objective function wrt 
+#'        to the parameters will be used. This is strongly recommended to achieve faster results.
 #' @return a list containing possibly the following elements:
 #' \item{\code{w}}{optimal portfolio vector}
 #' \item{\code{risk_contribution}}{the risk contribution of every asset}
 #' \item{\code{theta}}{the optimal value for theta (in case that it is part of
-#'                     the chosen formulation}
+#'                     the chosen formulation)}
 #' \item{\code{obj_fun}}{the sequence of values from the objective function at
 #'                       each iteration}
-#' \item{\code{risk}}{the last value of the objective function}
+#' \item{\code{risk}}{the last value of the generalized risk}
 #' \item{\code{mean_return}}{the expected return of the portoflio if the mean
 #'                           return term is included in the optimization}
 #' \item{\code{variance}}{the variance of the portfolio if the variance term is
 #'                        included in the optimization}
 #' \item{\code{elapsed_time}}{elapsed time recorded at every iteration}
 #' \item{\code{convergence}}{flag to indicate whether or not the optimization
-#' converged. The value `1` means it has converged, and `0` otherwise.}
+#' converged. The value \code{TRUE} means it has converged and \code{FALSE} otherwise.}
 #'
 #' @examples
 #' library(riskParityPortfolio)

README.Rmd

@@ -35,12 +35,12 @@ knit_hooks$set(pngquant = hook_pngquant)
 [![Docker Build Status](https://img.shields.io/docker/build/mirca/riskparityportfolio.svg)](https://hub.docker.com/r/mirca/riskparityportfolio/)
 
 
-*riskParityPortfolio* provides tools to design portfolios that follow the risk
-parity criteria. More precisely we implement a Newton method proposed by
-Spinu (2013) and a Cyclical method proposed by Griveau-Billion (2013), which
-formulate the risk parity as a convex problem and therefore a unique solution
-is available. For general, usually nonconvex formulations, we implement the
-successive convex approximation method proposed by Feng & Palomar (2015).
+The package *riskParityPortfolio* provides tools to design risk-parity 
+portfolios. In its simplest form, we consider the convex formulation 
+with a unique solution proposed by Spinu (2013) and use a cyclical 
+method inspired by Griveau-Billion (2013). For more general formulations, 
+which are usually nonconvex, we implement the successive convex approximation 
+method proposed by Feng & Palomar (2015).
 
 
 ## Installation
@@ -58,7 +58,7 @@ package?riskParityPortfolio
 ?riskParityPortfolio
 
 # Citing this work
-citation("library(riskParityPortfolio)")
+citation("riskParityPortfolio")
 
 ```
 

inst/CITATION

@@ -6,7 +6,7 @@ citEntry(entry = "Manual",
                                    as.person("D. P. Palomar")),
          note         = "R package version 0.1.0",
          year         = "2018",
-         url          = "https://CRAN.R-project.org/package=sparseIndexTracking",
+         url          = "https://CRAN.R-project.org/package=riskParityPortfolio",
 
          textVersion  =
            paste("J. V. de M. Cardoso and D. P. Palomar (2018).",