similar to: new version of samr package

Does anyone know of an R interface to Christian Borgelt's implementation of the FP growth algorithm? thanks a lot Rob Tibshirani -- I get so much email that I might not reply to an incoming email, just because it got lost. So don't hesitate to email me again. The probability of a reply should increase. Prof. Robert Tibshirani ?Depts of Health Research and Policy, and Statistics

This update adds an intercept option (by popular request) - now one can fit a model without an intercept Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net" regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast. The current list of models covered are:

This update adds an intercept option (by popular request) - now one can fit a model without an intercept Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net" regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast. The current list of models covered are:

Hi, I am trying to using SAMseq() to analyze my RNA-seq experiment (20000 genes x 550 samples) with survival endpoint. It quickly give the following error: > library(samr)Loading required package: imputeLoading required package: matrixStats Attaching package: ?matrixStats? The following objects are masked from ?package:Biobase?: ? ? anyMissing, rowMedians Warning messages:1: package ?samr? was

Sorry forgot to use plain text format, hope this time it works: Hi, I am trying to using SAMseq() to analyze my RNA-seq experiment (20000 genes x 550 samples) with survival endpoint. It quickly give the following error: > library(samr) Loading required package: impute Loading required package: matrixStats Attaching package: ?matrixStats? The following objects are masked from

Short course: Statistical Learning and Data Mining Trevor Hastie and Robert Tibshirani, Stanford University Sheraton Hotel Palo Alto, CA Feb 26-27, 2004 This two-day course gives a detailed overview of statistical models for data mining, inference and prediction. With the rapid developments in internet technology, genomics and other high-tech industries, we rely increasingly more on data

Short course: Statistical Learning and Data Mining Trevor Hastie and Robert Tibshirani, Stanford University Georgetown University Conference Center Washington DC September 20-21, 2004 This two-day course gives a detailed overview of statistical models for data mining, inference and prediction. With the rapid developments in internet technology, genomics and other high-tech industries, we

Short course: Statistical Learning and Data Mining Trevor Hastie and Robert Tibshirani, Stanford University Sheraton Hotel, Palo Alto, California February 24 & 25, 2005 This two-day course gives a detailed overview of statistical models for data mining, inference and prediction. With the rapid developments in internet technology, genomics and other high-tech industries, we rely

Rob Tibshirani and I are offering a MOOC in January on Statistical Learning. This “massive open online course" is free, and is based entirely on our new book “An Introduction to Statistical Learning with Applications in R” (James, Witten, Hastie, Tibshirani 2013, Springer). http://www-bcf.usc.edu/~gareth/ISL/ The pdf of the book will also be free. The course, hosted on Open edX, consists of

I have uploaded a new package to CRAN called ProDenICA. This fits ICA models directly via product-density estimation of the source densities. This package was promised on page 567 in the 2nd edition of our book 'Elements of Statistical Learning' (Hastie, Tibshirani and Friedman, 2009, Springer) . Apologies that it is so late. The method fits each source density by a tilted gaussian

I have uploaded a new package to CRAN called ProDenICA. This fits ICA models directly via product-density estimation of the source densities. This package was promised on page 567 in the 2nd edition of our book 'Elements of Statistical Learning' (Hastie, Tibshirani and Friedman, 2009, Springer) . Apologies that it is so late. The method fits each source density by a tilted gaussian

Hi, see these links: http://www.liacc.up.pt/~ltorgo/DataMiningWithR/ http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-sp-1/sp-1-page45.html Brian D. Ripley, Datamining: Large Databases and Methods, in Proceedings of "useR! 2004 - The R User Conference", may 2004 http://www.ci.tuwien.ac.at/Conferences/useR-2004/Keynotes/Ripley.pdf looking for a book I suggest: Trevor Hastie , Robert

Hi, I am using the superpc package. By superpc.train (data, type="regression") I calculated the standardized regression coefficients for measuring the univariate effect of each feature on a continuous response y. By superpc.cv(compute.fullcv=TRUE, compute.preval=FALSE, n.components=3, n.fold=10) I used cross validation to estimate the optimal feature threshold and choose only

Hi Thomas, see these papers or books (some are available on the web): Diego Kuonen, Introduction au data mining avec R : vers la reconqu??te du `knowledge discovery in databases' par les statisticiens. Bulletin of the Swiss Statistical Society, 40:3-7, 2001. Consultabile all??indirizzo web: http://www.statoo.com/en/publications/2001.R.SSS.40/ Diego Kuonen and Reinhard Furrer, Data mining

Least Angle Regression software: LARS "Least Angle Regression" ("LAR") is a new model selection algorithm; a useful and less greedy version of traditional forward selection methods. LAR is described in detail in a paper by Brad Efron, Trevor Hastie, Iain Johnstone and Rob Tibshirani, soon to appear in the Annals of Statistics. The paper, as well as R and Splus packages, are

Least Angle Regression software: LARS "Least Angle Regression" ("LAR") is a new model selection algorithm; a useful and less greedy version of traditional forward selection methods. LAR is described in detail in a paper by Brad Efron, Trevor Hastie, Iain Johnstone and Rob Tibshirani, soon to appear in the Annals of Statistics. The paper, as well as R and Splus packages, are

We have submitted glmnet_1.6 to CRAN This version has an improved convergence criterion, and it also uses a variable screening algorithm that dramatically reduces the time to convergence (while still producing the exact solutions). The speedups in some cases are by a factors of 20 to 50, depending on the particular problem and loss function. See our paper

We have submitted glmnet_1.6 to CRAN This version has an improved convergence criterion, and it also uses a variable screening algorithm that dramatically reduces the time to convergence (while still producing the exact solutions). The speedups in some cases are by a factors of 20 to 50, depending on the particular problem and loss function. See our paper

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 mining, inference and prediction. With the rapid developments in internet technology, genomics, financial

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 mining, inference and prediction. With the rapid developments in internet technology, genomics, financial