## All functions

BinomialExample

Synthetic dataset with binary response

Cindex()

compute C index for a Cox model

CoxExample

Synthetic dataset with right-censored survival response

MultiGaussianExample

Synthetic dataset with multiple Gaussian responses

MultinomialExample

Synthetic dataset with multinomial response

PoissonExample

Synthetic dataset with count response

QuickStartExample

Synthetic dataset with Gaussian response

SparseExample

Synthetic dataset with sparse design matrix

assess.glmnet() confusion.glmnet() roc.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

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

cox_obj_function()

Elastic net objective function value for Cox regression model

coxgrad()

Compute gradient for Cox model

coxnet.deviance()

Compute deviance for Cox model

cv.glmnet()

Cross-validation for glmnet

dev_function()

Elastic net deviance value

deviance(<glmnet>)

Extract the deviance from a glmnet object

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-package

Elastic net model paths for some generalized linear models

glmnet() relax.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

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(<cv.relaxed>)

plot the cross-validation curve produced by cv.glmnet

plot(<glmnet>) plot(<mrelnet>) plot(<multnet>) plot(<relaxed>)

plot coefficients from a "glmnet" object

predict(<cv.glmnet>) predict(<cv.relaxed>)

make predictions from a "cv.glmnet" object.

coef(<glmnet>) predict(<glmnet>) predict(<relaxed>)

Extract coefficients from a glmnet object

predict(<glmnetfit>)

Get predictions from a glmnetfit fit object

print(<cv.glmnet>)

print a cross-validated glmnet object

print(<glmnet>)

print a glmnet object

response.coxnet()

Make response for coxnet

rmult()

Generate multinomial samples from a probability 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