similar to: New package for ICA uploaded to CRA

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

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

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

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

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 contributed a "gam" library to CRAN, which implements "Generalized Additive Models". This implementation follows closely the description in the GAM chapter 7 of the "white" book "Statistical Models in S" (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy in "Generalized Additive Models" (Hastie & Tibshirani 1990,

I have contributed a "gam" library to CRAN, which implements "Generalized Additive Models". This implementation follows closely the description in the GAM chapter 7 of the "white" book "Statistical Models in S" (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy in "Generalized Additive Models" (Hastie & Tibshirani 1990,

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:

Yo me he apuntado y me parece bien tanto la idea de reunirse, como la de comentar a través de la lista. >________________________________ > De: "r-help-es-request@r-project.org" <r-help-es-request@r-project.org> >Para: r-help-es@r-project.org >Enviado: Viernes 10 de enero de 2014 12:00 >Asunto: Resumen de R-help-es, Vol 59, Envío 5 > > >Envíe los

Hola a todos, Gracias por avisar Carlos. Intentaré formar un grupo en Logroño. Belén Cillero Jiménez Técnico de Estadística Instituto de Estadística de La Rioja bcillero en larioja.org o?s?? ol ??d???s s???? ou ,so?u??s?p sop??lns?? s??snq ?S ________________________________________ De: r-help-es-bounces en r-project.org [r-help-es-bounces en r-project.org] en nombre de r-help-es-request

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

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