glmnet/ 0000755 0001762 0000144 00000000000 14471023421 011536 5 ustar ligges users glmnet/NAMESPACE 0000644 0001762 0000144 00000005232 14410376106 012763 0 ustar ligges users # Generated by roxygen2: do not edit by hand
S3method("[",stratifySurv)
S3method(buildPredmat,array)
S3method(buildPredmat,coxnetlist)
S3method(buildPredmat,default)
S3method(buildPredmat,glmnetfitlist)
S3method(buildPredmat,lognetlist)
S3method(buildPredmat,mrelnetlist)
S3method(buildPredmat,multnetlist)
S3method(coef,cv.glmnet)
S3method(coef,cv.relaxed)
S3method(coef,glmnet)
S3method(coef,relaxed)
S3method(deviance,glmnet)
S3method(family,cv.glmnet)
S3method(family,glmnet)
S3method(family,glmnetfit)
S3method(family,relaxed)
S3method(plot,cv.glmnet)
S3method(plot,cv.relaxed)
S3method(plot,glmnet)
S3method(plot,mrelnet)
S3method(plot,multnet)
S3method(plot,relaxed)
S3method(predict,coxnet)
S3method(predict,cv.glmnet)
S3method(predict,cv.relaxed)
S3method(predict,elnet)
S3method(predict,fishnet)
S3method(predict,glmnet)
S3method(predict,glmnetfit)
S3method(predict,lognet)
S3method(predict,mrelnet)
S3method(predict,multnet)
S3method(predict,relaxed)
S3method(print,bigGlm)
S3method(print,confusion.table)
S3method(print,cv.glmnet)
S3method(print,cv.relaxed)
S3method(print,glmnet)
S3method(print,relaxed)
S3method(survfit,coxnet)
S3method(survfit,cv.glmnet)
export(Cindex)
export(assess.glmnet)
export(bigGlm)
export(buildPredmat)
export(coef.glmnet)
export(coef.relaxed)
export(confusion.glmnet)
export(coxgrad)
export(coxnet.deviance)
export(cv.glmnet)
export(glmnet)
export(glmnet.control)
export(glmnet.measures)
export(makeX)
export(na.replace)
export(na_sparse_fix)
export(predict.glmnet)
export(predict.relaxed)
export(prepareX)
export(print.cv.glmnet)
export(relax.glmnet)
export(rmult)
export(roc.glmnet)
export(stratifySurv)
import(Matrix)
import(foreach)
import(methods)
importFrom(Matrix,sparse.model.matrix)
importFrom(Rcpp,sourceCpp)
importFrom(grDevices,rainbow)
importFrom(graphics,abline)
importFrom(graphics,axis)
importFrom(graphics,matplot)
importFrom(graphics,par)
importFrom(graphics,plot)
importFrom(graphics,points)
importFrom(graphics,segments)
importFrom(graphics,text)
importFrom(shape,colorlegend)
importFrom(stats,approx)
importFrom(stats,as.formula)
importFrom(stats,binomial)
importFrom(stats,coef)
importFrom(stats,contrasts)
importFrom(stats,family)
importFrom(stats,gaussian)
importFrom(stats,glm)
importFrom(stats,glm.fit)
importFrom(stats,median)
importFrom(stats,model.matrix)
importFrom(stats,na.pass)
importFrom(stats,predict)
importFrom(stats,rmultinom)
importFrom(stats,rnorm)
importFrom(stats,runif)
importFrom(stats,weighted.mean)
importFrom(survival,Surv)
importFrom(survival,concordance)
importFrom(survival,coxph)
importFrom(survival,is.Surv)
importFrom(survival,strata)
importFrom(survival,survfit)
importFrom(utils,packageDescription)
useDynLib(glmnet)
glmnet/README.md 0000644 0001762 0000144 00000007453 14405701475 013037 0 ustar ligges users
# Lasso and Elastic-Net Regularized Generalized Linear Models
[](https://cran.r-project.org/package=glmnet)[](https://CRAN.R-project.org/package=glmnet)
We provide extremely efficient procedures for fitting the entire lasso
or elastic-net regularization path for linear regression (gaussian),
multi-task gaussian, logistic and multinomial regression models (grouped
or not), Poisson regression and the Cox model. The algorithm uses
cyclical coordinate descent in a path-wise fashion. Details may be found
in Friedman, Hastie, and Tibshirani ([2010](#ref-glmnet)), Simon et al.
([2011](#ref-coxnet)), Tibshirani et al. ([2012](#ref-strongrules)),
Simon, Friedman, and Hastie ([2013](#ref-block)).
Version 3.0 is a major release with several new features, including:
- Relaxed fitting to allow models in the path to be refit without
regularization. CV will select from these, or from specified
mixtures of the relaxed fit and the regular fit;
- Progress bar to monitor computation;
- Assessment functions for displaying performance of models on test
data. These include all the measures available via `cv.glmnet`, as
well as confusion matrices and ROC plots for classification models;
- print methods for CV output;
- Functions for building the `x` input matrix for `glmnet` that allow
for *one-hot-encoding* of factor variables, appropriate treatment of
missing values, and an option to create a sparse matrix if
appropriate.
- A function for fitting unpenalized a single version of any of the
GLMs of `glmnet`.
Version 4.0 is a major release that allows for any GLM family, besides
the built-in families.
Version 4.1 is a major release that expands the scope for survival
modeling, allowing for (start, stop) data, strata, and sparse X inputs.
It also provides a much-requested method for `survival:survfit`.
## References