| glmnet-package | Elastic net model paths for some generalized linear models |
| assess.glmnet | assess performance of a 'glmnet' object using test data. |
| beta_CVX | Simulated data for the glmnet vignette |
| bigGlm | fit a glm with all the options in 'glmnet' |
| Cindex | compute C index for a Cox model |
| coef.cv.glmnet | make predictions from a "cv.glmnet" object. |
| coef.cv.relaxed | make predictions from a "cv.glmnet" object. |
| coef.glmnet | Extract coefficients from a glmnet object |
| coef.relaxed | Extract coefficients from a glmnet object |
| confusion.glmnet | assess performance of a 'glmnet' object using test data. |
| coxgrad | compute gradient for cox model |
| coxnet.deviance | compute deviance for cox model output |
| cv.glmnet | Cross-validation for glmnet |
| deviance.glmnet | Extract the deviance from a glmnet object |
| dev_function | Elastic net deviance value |
| elnet.fit | Solve weighted least squares (WLS) problem for a single lambda value |
| get_start | Get null deviance, starting mu and lambda max |
| glmnet | fit a GLM with lasso or elasticnet regularization |
| glmnet.control | internal glmnet parameters |
| glmnet.fit | Fit a GLM with elastic net regularization for a single value of lambda |
| glmnet.measures | Display the names of the measures used in CV for different "glmnet" families |
| glmnet.path | Fit a GLM with elastic net regularization for a path of lambda values |
| makeX | convert a data frame to a data matrix with one-hot encoding |
| na.replace | Replace the missing entries in a matrix columnwise with the entries in a supplied vector |
| obj_function | Elastic net objective function value |
| pen_function | Elastic net penalty value |
| plot.cv.glmnet | plot the cross-validation curve produced by cv.glmnet |
| plot.cv.relaxed | plot the cross-validation curve produced by cv.glmnet |
| plot.glmnet | plot coefficients from a "glmnet" object |
| plot.mrelnet | plot coefficients from a "glmnet" object |
| plot.multnet | plot coefficients from a "glmnet" object |
| plot.relaxed | plot coefficients from a "glmnet" object |
| predict.coxnet | Extract coefficients from a glmnet object |
| predict.cv.glmnet | make predictions from a "cv.glmnet" object. |
| predict.cv.relaxed | make predictions from a "cv.glmnet" object. |
| predict.elnet | Extract coefficients from a glmnet object |
| predict.fishnet | Extract coefficients from a glmnet object |
| predict.glmnet | Extract coefficients from a glmnet object |
| predict.glmnetfit | Get predictions from a 'glmnetfit' fit object |
| predict.lognet | Extract coefficients from a glmnet object |
| predict.mrelnet | Extract coefficients from a glmnet object |
| predict.multnet | Extract coefficients from a glmnet object |
| predict.relaxed | Extract coefficients from a glmnet object |
| print.bigGlm | print a glmnet object |
| print.cv.glmnet | print a cross-validated glmnet object |
| print.cv.relaxed | print a cross-validated glmnet object |
| print.glmnet | print a glmnet object |
| print.relaxed | print a glmnet object |
| relax.glmnet | fit a GLM with lasso or elasticnet regularization |
| rmult | Generate multinomial samples from a probability matrix |
| roc.glmnet | assess performance of a 'glmnet' object using test data. |
| spelnet.fit | Solve weighted least squares (WLS) problem for a single lambda value |
| spglmnet.fit | Fit a GLM with elastic net regularization for a single value of lambda |
| x | Simulated data for the glmnet vignette |
| y | Simulated data for the glmnet vignette |