similar to: glmnet_1.5.1 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

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

Hi, I am trying to do multinomial regression using the glmnet package, but the following gives me an error (for no reason apparent to me): library(glmnet) cv.glmnet(x=matrix(c(1,2,3,4,5,6,1,2,3,4,5,6), nrow=6),y=as.factor(c(1,2,1,2,3,3)),family='multinomial',alpha=0.5, nfolds=2) The error i get is: Error in if (outlist$msg != "Unknown error") return(outlist) : argument is of

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:

Background: I administer a cluster of RedHat EWS 3U4 Linux workstations at a university. I built R 2.1.0 from source: ./configure \ --prefix=/sscc/opt/R-2.1.0 \ --with-blas=no \ 2>&1 \ | tee NUInstall.configure R is now configured for i686-pc-linux-gnu Source directory: . Installation directory: /sscc/opt/R-2.1.0 C compiler:

Hi, in R-3.0 I get the following error when calling lsfit with more observations than variables, which seems to come from an error in the formatting of the error message (note that this was not happening in 2.15.3): > nobs <- 5; nvar <- 6; lsfit(matrix(runif(nobs*nvar), ncol=nvar), runif(nobs), intercept=FALSE) Error in sprintf(ngettext(nry, "%d response", "%d

Hi, My apologies for asking this question, but could not find the answer elsewhere. I understand the glmnet package uses Fortran code. For example, the lognet.R file includes the lines of code shown below. But how can I see the Fortran code that is being referenced in the code below? Is that provided somewhere in the package source code? .Fortran("lognet",

Hello R-help list, I have a piece of code written by a grad student here at BU which will segfault when using one data set, but complete just fine using another. Both sets are just text files full of real numbers. It seems like a bug within R. It could be a bug within her data, but again, her data is just a bunch of floats, so her data could be triggering a bug within R. I have tried this

This new version of glmnet has some bug fixes, and some new features * new arguments lower.limits=-Inf and upper.limits=Inf (defaults shown) for all the coefficients in glmnet. Users can provide limits on coefficients. See the documentation for glmnet. Typical usage: glmnet(x,y,lower=0) Here the argument is abbreviated, and by giving a single value, this uses the same value for all parameters.

This new version of glmnet has some bug fixes, and some new features * new arguments lower.limits=-Inf and upper.limits=Inf (defaults shown) for all the coefficients in glmnet. Users can provide limits on coefficients. See the documentation for glmnet. Typical usage: glmnet(x,y,lower=0) Here the argument is abbreviated, and by giving a single value, this uses the same value for all parameters.

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

I will be giving a webinar on glmnet on Friday May 3, 2013 at 10am PDT (pacific daylight time) The one-hour webinar will consist of: - Intro to lasso and elastic net regularization, and coefficient paths - Why is glmnet so efficient and flexible - New features of the latest version of glmnet - Live glmnet demonstration - Question and Answer period To sign up for the webinar, please go to

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

Hello, I got an error message saying Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : NA/NaN/Inf in foreign function call (arg 5) when I try to analysis a binary trait using glmnet(R) by running the following code library(glmnet) Xori <- read.table("c:\\SNP.txt", sep='\t'); Yori <- read.table("c:\\Trait.txt", sep=',');