[Returnanalytics-commits] r2202 - pkg/PerformanceAnalytics/sandbox/Meucci/demo

[noreply at r-forge.r-project.org]

Author: mkshah
Date: 2012-07-25 09:01:16 +0200 (Wed, 25 Jul 2012)
New Revision: 2202

Added:
   pkg/PerformanceAnalytics/sandbox/Meucci/demo/00index
Modified:
   pkg/PerformanceAnalytics/sandbox/Meucci/demo/InvariantProjection.R
Log:
Adding Index File for demo folder and deleting unnecessary code in InvariantProjection.R

Added: pkg/PerformanceAnalytics/sandbox/Meucci/demo/00index
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/demo/00index	                        (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/demo/00index	2012-07-25 07:01:16 UTC (rev 2202)
@@ -0,0 +1,15 @@
+AnalyticalvsNumerical       This example script compares the numerical and the analytical solution of entropy-pooling
+ButterflyTrading            This example script performs the butterfly-trading case study for the Entropy-Pooling approach by Attilio Meucci
+DetectOutliersviaMVE        This example script detects outliers in two-asset and multi-asset case
+FullyFlexibleBayesNet       This case study uses Entropy Pooling to compute Fully Flexible Bayesian networks for risk management
+HermiteGrid_CaseStudy       This script estimates the prior of a hedge fund return and processes extreme views on CVaR according to Entropy Pooling
+HermiteGrid_CVaR_Recursion  This script illustrates the discrete Newton recursion  to process views on CVaR according to Entropy Pooling
+HermiteGrid_demo            This script compares the performance of plain Monte Carlo versus grid in applying Entropy Pooling to process extreme views
+InvariantProjection         This script projects summary statistics to arbitrary horizons under i.i.d. assumption
+logToArithmeticCovariance   This example script generates arithmetric returns and arithmetric covariance matrix given a distribution of log returns
+Prior2Posterior             This example script compares the numerical and the analytical solution of entropy-pooling
+RankingInformation          This script performs ranking allocation using the Entropy-Pooling approach by Attilio Meucci
+RobustBayesianAllocation    This script replicates the example from Meucci's MATLAB script S_SimulationsCaseStudy.M
+S_plotGaussHermite          This example script displays mesh points based on Gaussian-Hermite quadrature
+S_SnPCaseStudy              This script replicates the example from Meucci's MATLAB script S_SnPCaseStudy.M
+S_ToyExample                This toy example illustrates the use of Entropy Pooling to compute Fully Flexible Bayesian networks
\ No newline at end of file

Modified: pkg/PerformanceAnalytics/sandbox/Meucci/demo/InvariantProjection.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/demo/InvariantProjection.R	2012-07-23 20:05:47 UTC (rev 2201)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/demo/InvariantProjection.R	2012-07-25 07:01:16 UTC (rev 2202)
@@ -1,22 +1,9 @@
 # Annualization and Projection algorithm for invariant
 #
-# Project summary statistics to arbitrary horizons under i.i.d. assumption
 # SYMMYS - Last version of article and code available at http://symmys.com/node/136
 # Project summary statistics to arbitrary horizons under i.i.d. assumption
 # see Meucci, A. (2010) "Annualization and General Projection of Skewness, Kurtosis and All Summary Statistics"
 # GARP Risk Professional, August, pp. 52-54
-#
-# @param    N    
-# @param    K    
-# @param    X    a numeric vector consisting of a generic (additive) invariant the 
-#                  follows the general linear and square-root rules for projecting means and volatility
-#
-# @return   Ga   a numeric vector with the first 'N' order statistics projected to the horizon 'K'
-# @export
-# @author Ram Ahluwalia \email{rahluwalia@@gmail.com}
-# @examples
-#           X = GenerateLogNormalDistribution( J = 100000 , a = 01 , m = .2 , s = .4 ) # X = a + exp( m + s * Z ) # generate log-normal distribution
-#           moments = ProjectInvariant( N = 6 , K = 251 , X )
 
 N = 6   # a numeric with the number of the first N stadardized summary statistics to project
 K = 100 # a numeric with an arbitrary projection horizon