Package index • quadrupen

Main functions for user

Functions for fitting various structured regularization models

bounded_reg() bounded.reg(): Fit a linear model with infinity-norm plus ridge-like regularization
sparse_lm() elastic.net() elastic_net() lasso() mcp() scad(): Fit a linear model with sparse regularization
fused_lasso(): A function for fitting generalized fused-Lasso problems
group_sparse_lm() group_lasso() group_l1linf() coop_lasso() sparse_group_lasso() sparse_group_l1linf() sparse_coop_lasso(): Fit a linear model with (sparse) group regularisation
group_lava(): Fit a linear model with group-lava regularization
lava(): Fit a linear model with lava regularization
ridge(): Fit a linear model with a structured ridge regularization

R6 Classes for the user to manipulate the ouput of the main functions

R6 Classes used as fields of the main Class Quadrupen fit

S3 methods, aliases for the most useful R6 methods.

criteria(): Penalized criteria based on estimation of degrees of freedom
cross_validate(): Cross-validation for Quadrupen object
stability(): Stability selection for Quadrupen object
selection(): Variable selection from a stability path
plot(<QuadrupenFit>) plot(<CrossValidation>) plot(<StabilityPath>): Plot method for quadrupen objects
residuals(<QuadrupenFit>): Extract model residuals
coef(<QuadrupenFit>): Extract model coefficients
predict(<QuadrupenFit>): Perform model prediction
fitted(<QuadrupenFit>): Extracts model fitted values
deviance(<QuadrupenFit>): Extract model deviance
isQuadrupenFit(): Auxiliary functions to check the given class of an object