# Lasso and Elastic-Net Regularized Generalized Linear Models

## Documentation for package ‘glmnet’ version 4.1-4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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'
BinomialExample | Synthetic dataset with binary response
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.
cox.fit | Fit a Cox regression model with elastic net regularization for a single value of lambda
cox.path | Fit a Cox regression model with elastic net regularization for a path of lambda values
CoxExample | Synthetic dataset with right-censored survival response
coxgrad | Compute gradient for Cox model
coxnet.deviance | Compute deviance for Cox model
cox_obj_function | Elastic net objective function value for Cox regression model
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
fid | Helper function for Cox deviance and gradient
get_cox_lambda_max | Get lambda max for Cox regression model
get_eta | Helper function to get etas (linear predictions)
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
MultiGaussianExample | Synthetic dataset with multiple Gaussian responses
MultinomialExample | Synthetic dataset with multinomial response
mycoxph | Helper function to fit coxph model for survfit.coxnet
mycoxpred | Helper function to amend ... for new data in survfit.coxnet
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
PoissonExample | Synthetic dataset with count response
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
QuickStartExample | Synthetic dataset with Gaussian response
relax.glmnet | fit a GLM with lasso or elasticnet regularization
response.coxnet | Make response for coxnet
rmult | Generate multinomial samples from a probability matrix
roc.glmnet | assess performance of a 'glmnet' object using test data.
SparseExample | Synthetic dataset with sparse design matrix
stratifySurv | Add strata to a Surv object
survfit.coxnet | Compute a survival curve from a coxnet object
survfit.cv.glmnet | Compute a survival curve from a cv.glmnet object
use.cox.path | Check if glmnet should call cox.path
weighted_mean_sd | Helper function to compute weighted mean and standard deviation
x | Simulated data for the glmnet vignette
y | Simulated data for the glmnet vignette