merge README edits from Doug Martin ahead of CRAN release

README

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-A primary addition to PortfolioAnalytics in this 2.0 release is the integration of the CVXR solver R package for convex optimization. See CVXR supports eleven solvers, each of which supports solvers for one or more of the following optimization problems: LP, QP, SOCP, SDP, EXP, MIP. See the Table near the beginning of the  document “Convex Optimization in R” at https://cvxr.rbind.io/ . Thus, with PortfolioAnalytics 2.0, users are able to use any one of  a large variety of solvers available in CVXR for their portfolio optimization problems.
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-A particular use of CVXR in PortfolioAnalytics 2.0 is for computing Minimum Expected Quadratic Shortfall (MinEQS) portfolios, which is a second-order cone programming (SOCP) optimization problem. This is quite a new capability not available in other portfolio optimization software products. Details are provided in the Vignette “cvxrPortfolioAnalytics.pdf” Vignette listed near the end of this document.
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-Another important feature of PortfolioAnalytics 2.0, is that it contains functionality for computing robust mean variance optimal (MVO) portfolios, using any one of several robust covariance matrix estimators that are not much influenced by outliers  Details are provided in the Vignette “robustCovMatForPA.pdf” listed near the end of this document.
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-Specific new features in PortfolioAnalytics 2.0 include the following New Functions, Enhanced Functions, S3 Support Methods, Custom Moment Functions, and New Vignettes, listed below.
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-<<<<<<< HEAD
-=======
 A primary addition to PortfolioAnalytics in this 2.0 release is the integration 
 of the CVXR solver R package for convex optimization. See CVXR supports eleven 
 solvers, each of which supports solvers for one or more of the following 
@@ -26,7 +16,6 @@ Another important feature of PortfolioAnalytics 2.0, is that it contains
 functionality for computing robust mean variance optimal (MVO) portfolios, using 
 any one of several robust covariance matrix estimators that are not much influenced 
 by outliers  Details are provided in the Vignette “robustCovMatForPA”.
->>>>>>> d922677 (merge README edits from Doug Martin ahead of CRAN submission)
 
 New PortfolioAnalytics Functions:
 
@@ -69,13 +58,6 @@ New Vignettes and their Code Functions in the demo Folder:
 3. robustCovMatForPA: CRAN title = “Robust Covariance Matrices for PortfolioAnalytics”
 4. demo_robustCovMatForPA.R   
 
-Please contribute with bug fixes, comments, and testing scripts. In doing so please use data disguising it as need be, or use data sets like 'edhec' which is available in the PerformanceAnalytics package, or like ‘stocksCRSP’  and ‘factorsSPGMI’ in the PCRA package.
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-<<<<<<< HEAD
-Acknowledgements
-
-The bulk of the work in creating PortfolioAnalytics 2.0 was done by Xinran Zhao, along with contributions from Yifu Kang, under the support of a 2022 Google Summer of Code (GSOC 2022). Xinran and Yifu were mentored in GSOC 2022 by Professor Doug Martin and Professor Steve Murray in the Applied Mathematics Department at the University of Washington.
-=======
 Please contribute with bug fixes, comments, and testing scripts! 
 (please take your data and disguise it, or use data sets like 'edhec' like we 
 do in the demo or or like ‘stocksCRSP’  and ‘factorsSPGMI’ in the PCRA package 
@@ -92,4 +74,3 @@ along with contributions from Yifu Kang, under the support of a 2022 Google
 Summer of Code (GSOC 2022). Xinran and Yifu were mentored in GSOC 2022 by 
 Professor Doug Martin and Professor Steve Murray in the Applied Mathematics 
 Department at the University of Washington.
->>>>>>> d922677 (merge README edits from Doug Martin ahead of CRAN submission)