similar to: glmnet_1.6 uploaded to CRAN

This is a new version of glmnet, that incorporates some bug fixes and speedups. * a new convergence criterion which which offers 10x or more speedups for saturated fits (mainly effects logistic, Poisson and Cox) * one can now predict directly from a cv.object - see the help files for cv.glmnet and predict.cv.glmnet * other new methods are deviance() for "glmnet" and coef() for

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

It would be nice to have a date stamp on an object. In S/Splus this was always available, because objects were files. I have looked around, but I presume this information is not available. -------------------------------------------------------------------- Trevor Hastie hastie at stanford.edu Professor, Department of Statistics, Stanford University Phone:

This works: > model.matrix(~I(pos>3),data=data.frame(pos=c(1:5))) (Intercept) I(pos > 3)TRUE 1 1 0 2 1 0 3 1 0 4 1 1 5 1 1 attr(,"assign") [1] 0 1 attr(,"contrasts") attr(,"contrasts")$"I(pos > 3)" [1] "contr.treatment"

In glmnet_1.5 a poor default was set for the argument type which caused the program to be very slow or even crash when nvar (p) is very large. The argument type (now called type.gaussian) has two options, "covariance" or "naive", and is used for the default family="gaussion" model (squared error loss). When type.gaussian="covariance", all inner-products

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

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

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

glmnet is a package that fits the regularization path for linear, two- and multi-class logistic regression models with "elastic net" regularization (tunable mixture of L1 and L2 penalties). glmnet uses pathwise coordinate descent, and is very fast. Some of the features of glmnet: * by default it computes the path at 100 uniformly spaced (on the log scale) values of the

glmnet is a package that fits the regularization path for linear, two- and multi-class logistic regression models with "elastic net" regularization (tunable mixture of L1 and L2 penalties). glmnet uses pathwise coordinate descent, and is very fast. Some of the features of glmnet: * by default it computes the path at 100 uniformly spaced (on the log scale) values of the

glmnet_1.2 has been uploaded to CRAN. This is a major upgrade, with the following additional features: * poisson family, with dense or sparse x * Cox proportional hazards family, for dense x * wide range of cross-validation features. All models have several criteria for cross-validation. These include deviance, mean absolute error, misclassification error and "auc" for logistic or

glmnet_1.2 has been uploaded to CRAN. This is a major upgrade, with the following additional features: * poisson family, with dense or sparse x * Cox proportional hazards family, for dense x * wide range of cross-validation features. All models have several criteria for cross-validation. These include deviance, mean absolute error, misclassification error and "auc" for logistic or

I have posted an update to the GAM package. Note that this package implements gam() as described in the "White" S book (Statistical models in S). In particular, you can fit models with lo() terms (local regression) and/or s() terms (smoothing splines), mixed in, of course, with any terms appropriate for glms. A number of bugs in version 0.92 have been fixed; notably 1) some problems

I have posted an update to the GAM package. Note that this package implements gam() as described in the "White" S book (Statistical models in S). In particular, you can fit models with lo() terms (local regression) and/or s() terms (smoothing splines), mixed in, of course, with any terms appropriate for glms. A number of bugs in version 0.92 have been fixed; notably 1) some problems

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the