[R] Austria, September, 2009: Statistical Learning and Data Mining Course

Short course: Statistical Learning and Data Mining III: Ten Hot Ideas for Learning from Data Trevor Hastie and Robert Tibshirani, Stanford University Danube University Krems, Austria 25-26 September 2009 This two-day course gives a detailed overview of statistical models for data mining

[R] [R-pkgs] Major glmnet upgrade on CRAN

ta: N=144, p=16K, 14 class multinomial, 100 values along lasso path. Time = 30secs Authors: Jerome Friedman, Trevor Hastie, Rob Tibshirani. See our paper http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf for implementation details, and comparisons with ot

[R] [R-pkgs] New package for ICA uploaded to CRA

dvances in Neural Information Processing Systems 15, MIT Press, Cambridge, MA, pp. 649-656. --- Trevor Hastie has...@stanford.edu Professor, Department of Statistics, Stanford University Phone:

[R] help needed with help

am running: This is GNU Emacs 22.2.50.1 (i386-apple-darwin9.4.0, Carbon Version 1.6.0) of 2008-07-17 on seijiz.local ----------- Trevor Hastie has...@stanford.edu Professor, Department of Statistics, Stan

[R] new version of glmnet

ith > 99% zeros in X matrix), glmnet takes less than two minutes to fit the entire regularization path on a grid of 100 values of the reg. parameter lambda. For a 14-class gene expression dataset (144 obs, 16K vars, not sparse), it takes 15 seconds to fit the path at 100 values of

[R] New version of package mda

ular, earth works as a regression method for fda() and mda(). Trevor Hastie __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.

[R] Austria, September, 2009: Statistical Learning and Data Mining Course

Short course: Statistical Learning and Data Mining III: Ten Hot Ideas for Learning from Data Trevor Hastie and Robert Tibshirani, Stanford University Danube University Krems, Austria 25-26 September 2009 This two-day course gives a detailed overview of statistical models for data mining

[R] [R-pkgs] Some improvements in gam package

Trevor Hastie Begin forwarded message: > From: "Trevor Hastie" > Subject: gam --- a new contributed package > Date: August 6, 2004 10:35:36 AM PDT > To: > > I have contributed a "gam" library to CRAN, > which implements "Generalized Additive Mod

[R] [R-pkgs] glmnet_1.8-4 on CRAN

l argument type.multinomial=c("ungrouped","grouped") For the grouped cases, again a group lasso penalty is used on the set of class coefficients for a predictor. Trevor Hastie -------- T

[R] [R-pkgs] glmnet_1.9-1 submitted to CRAN

, the settings persist for the session. glmnet.control has a useful factory=TRUE argument, which will reset the "factory" defaults. * a memory bug in coxnet has been fixed. Trevor Hastie ---- Tre

[R] [R-pkgs] glmnet 1.9-3 uploaded to CRAN (with intercept option)

ong lasso path. Time = 30secs Authors: Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon References: Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent http://www.stanford.edu/~hastie/Pape

[R] [R-pkgs] softImpute_1.0 uploaded to CRAN

ctures and uses warm starts. Some of the methods used are described in Rahul Mazumder, Trevor Hastie and Rob Tibshirani: Spectral Regularization Algorithms for Learning Large Incomplete Matrices. JMLR 2010 11 2287-2322 Other newer and more efficient methods that inter-weave the alternating bl

[R] [R-pkgs] glmnet webinar Friday May 3 at 10am PDT

Trevor Hastie has...@stanford.edu Professor, Department of Statistics, Stanford University Phone: (650) 725-2231 Fax: (650) 725-8977 URL: http://www.stanford.edu/~hastie address

[R] MOOC on Statistical Learning with R

ourse webpage http://statlearning.class.stanford.edu/ to enroll and for for further details. ---- Trevor Hastie has...@stanford.edu Professor, Department of Statistics, Sta

[R] Statistical Learning and Datamining Course October 2010 Washington DC

Short course: Statistical Learning and Data Mining III: Ten Hot Ideas for Learning from Data Trevor Hastie and Robert Tibshirani, Stanford University Georgetown University Conference Center Washington DC, October 11-12, 2010. This two-day course gives a detailed overview of statistical

[R] glmnet_1.5 uploaded to CRAN

peed trials: Newsgroup data: N=11,000, p= 0.75 Million, two class logistic. 100 values along lasso path. Time = 2mins 14 Class cancer data: N=144, p=16K, 14 class multinomial, 100 values along lasso path. Time = 30secs Authors: Jerome Friedman, Trevor Hastie, Rob Tibshirani. See our paper http:/

[R] glmnet_1.5.1 uploaded to CRAN

.glmnet, which is now renamed to type.measure. In both cases, abbreviations work. --- Trevor Hastie has...@stanford.edu Professor, Department of Statistics, Stanford University Phone: (650) 725-2231 (Statistics)

[R] [R-pkgs] svmpath_0.95 uploaded to CRAN

This new version includes a plot method for plotting a particular instance along the path. Trevor Hastie has...@stanford.edu Professor, Department of Statistics

[R] [R-pkgs] glmnet_1.6 uploaded to CRAN

, depending on the particular problem and loss function. See our paper http://www-stat.stanford.edu/~tibs/ftp/strong.pdf "Strong Rules for Discarding Predictors in Lasso-type Problems" for details of this screening method. --- Tre

[R] Glmnet_1.8 uploaded to CRAN

e options. A report is in the works. -------- Trevor Hastie has...@stanford.edu Professor, Department of Statistics, Stanford University Phone: (650) 725-2231 Fax: (650) 725-8977 URL: http://www.stanford.edu/~hastie address: room 104, Dep

Re: [R] differences between 1.7 and 1.7.1 glmnet versions

I have just started using changelogs, and am clearly not disciplined enough at it. The big change that occurred was the convergence criterion, which would account for the difference. At some point will put up details of this. Trevor Hastie On Dec 26, 2011, at 11:55 PM, Damjan Krstajic wrote

[R] [R-pkgs] sparsenet: a new package for sparse model selection

We have put a new package sparsenet on CRAN. Sparsenet fits regularization paths for sparse model selection via coordinate descent, using a penalized least-squares framework and a non-convex penalty. The package is based on our JASA paper Rahul Mazumder, Jerome Friedman and Trevor Hastie

[R] glmnet_1.7.3 on windows

We are aware that glmnet_1.7.3 does not pass for windows and are looking into the problem. It has something to do with the gcc compiler being slightly different on windows versus linux/mac platforms. As soon as we have resolved the issue, we will post a new version to CRAN Trevor Hastie

[R] glmnet_1.7.4

linux or MacOS platforms. Trevor Hastie has...@stanford.edu Professor, Department of Statistics, Stanford University Phone: (650) 725-2231 Fax: (650) 725

[R] [R-pkgs] New glmnet package on CRAN

e = 30secs Authors: Jerome Friedman, Trevor Hastie, Rob Tibshirani. See our paper http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf for implementation details, and comparisons with other related software. -- ---- Tre

[R] New Statistical Learning and Data Mining Course

Short course: Statistical Learning and Data Mining III: Ten Hot Ideas for Learning from Data Trevor Hastie and Robert Tibshirani, Stanford University Sheraton Hotel Palo Alto, CA March 16-17, 2009 This two-day course gives a detailed overview of statistical models for data mining

[R] new version of glmnet

. Thanks to many users, esp. Tim Hesterberg, for notifying us of the errors. Trevor Hastie __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.htm

[R] Short Course: Statistical Learning and Data Mining

Short course: Statistical Learning and Data Mining II: tools for tall and wide data Trevor Hastie and Robert Tibshirani, Stanford University Sheraton Hotel, Palo Alto, California, April 3-4, 2006. This two-day course gives a detailed overview of statistical models for data

[R] Correction: Short Course: Statistical Learning and Data Mining

Apologies, my last email announcing this course had the wrong dates. Here is the corrected header: Short course: Statistical Learning and Data Mining II: tools for tall and wide data Trevor Hastie and Robert Tibshirani, Stanford University Sheraton Hotel, Palo Alto, California