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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 regularization parameter * glmnet appears to be faster than any of the packages that are freely available, in some cases by two orders...

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 regularization parameter * glmnet appears to be faster than any of the packages that are freely available, in some cases by two orders...

..." Here is the description of the package. glmnet is a package that fits the regularization path for linear, two- and multi-class logistic regression models, poisson regression and the Cox model, 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 regularization parameter * glmnet is very fast, even for large data sets. * recognizes and exploits sparse input matrices (ala Matrix pa...

...model Here is the description of the package. glmnet is a package that fits the regularization path for linear, two- and multi-class logistic regression models, poisson regression and the Cox model, 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 regularization parameter * glmnet appears to be faster than any of the packages that are freely available, in some cases by two orders o...

...model Here is the description of the package. glmnet is a package that fits the regularization path for linear, two- and multi-class logistic regression models, poisson regression and the Cox model, 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 regularization parameter * glmnet appears to be faster than any of the packages that are freely available, in some cases by two orders o...

...ate 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: least squares linear regression binary logistic regression multinomial logistic regression (grouped and ungrouped) poisson regression multi-response linear regression (grouped) Cox proportinal-hazards model Some of th...

...ate 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: least squares linear regression binary logistic regression multinomial logistic regression (grouped and ungrouped) poisson regression multi-response linear regression (grouped) Cox proportinal-hazards model Some of th...

...tcomes. * fanc (0.18) Maintainer: Kei Hirose Author(s): Kei Hirose, Michio Yamamoto License: GPL (>= 2) http://crantastic.org/packages/fanc This package computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used. * fishmove (0.0-1) Maintainer: Johannes Radinger Author(s): Johannes Radinger License: GPL (>= 2) http://crantastic.org/packages/fishmove Functions to predict fish movement parameters based on multiple regression and plotting lept...