Package index

Main functions for user

Functions for fitting various structured regularization models

bounded.reg()

Fit a linear model with infinity-norm plus ridge-like regularization

elastic.net()

Fit a linear model with elastic-net regularization

fusedlasso()

A function for fitting generalized fused-Lasso problems

group.lasso()

Fit a linear model with (sparse) group regularisation (either l1/l2, l1/l-inf or cooperative variant)

group.lava()

Fit a linear model with group-lava regularization

lava()

Fit a linear model with lava regularization

lasso()

Fit a linear model with lasso regularization

ridge()

Fit a linear model with a structured ridge regularization

Classes and methods for handling the Quadrupen fits

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

QuadrupenFit

Class "QuadrupenFit"

ElasticNetFit

Class "ElasticNetFit"

FusedLassoFit

Class "FusedLassoFit"

GroupLassoFit

Class "GroupLassoFit"

GroupLavaFit

Class "GroupLavaFit"

LavaFit

Class "LavaFit"

BoundedRegressionFit

Class "BoundedRegression"

RidgeRegressionFit

Class "RidgeRegressionFit"

Auxiliaries Classes and methods

R6 Classes used as fields of the main Class Quadrupen fit

DataModel

Data Class

CrossValidation

Class CrossValidation

StabilityPath

Class StabilityPath

InformationCriteria

Class InformationCriteria

Auxiliaries S3 methods

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

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