MATLAB TOOLBOX: envlp VERSION: 1.0 DATA: 04-Sep-2012 LICENSE: GPL-2 AUTHORS: Dennis Cook (dennis@stat.umn.edu) Zhihua Su (zhihuasu@stat.ufl.edu) Yi Yang (yiyang@umn.edu) URL: http://code.google.com/p/envlp/ Description ___________ A MATLAB Toolbox for Computing Envelope Estimators in Multivariate Linear Regression. The envlp toolbox implements a variety of models in the envelope area under the framework of multivariate linear regression or estimating the multivariate mean: 1. envelope model 2. partial envelope model 3. heteroscedastic envelope model 4. inner envelope model 5. scaled envelope model 6. envelope model in the predictor space 7. envelope model for estimating the multivariate mean Installation ____________ You can place the envlp directory anywhere. To install the toolbox, direct your MATLAB working directory to the folder `envlp/`, and type `install_envlp` If the users agree with our license statements, the installation is completed, and all the utilities of the toolbox are added to the MATLAB path. NOTE: If a previous version of the toolbox is present, it should be removed before installing the new version. To remove, simply delete the folder of the toolbox. Documentation _____________ You can find a very comprehensive user's guide and reference manual in the `envlp/doc/` subdirectory. Examples and Datasets _____________________ Trying some examples in the `envlp/examples/` subdirectory is a good way to check if envlp is installed properly. Running these examples requires datasets in `envlp/data/` subdirectory. See `envlp/data/README_data.txt` for further information. Technical support _________________ We provide a support website, on which the users can download the toolbox, report bugs and check recent updates http://code.google.com/p/envlp/. For further help, the users can contact the authors of the toolbox: Dennis Cook (dennis@stat.umn.edu), Zhihua Su (zhihuasu@stat.ufl.edu) and Yi Yang (yiyang@umn.edu). References __________ envlp: A MATLAB Toolbox for Computing Envelope Estimators in Multivariate Linear Regression. Dennis Cook, Zhihua Su, Yi Yang. Technical report (2012)