Function reference • glmnet

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

Reference

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