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lqa (version 1.0-3)

Penalized Likelihood Inference for GLMs

Description

This package provides some basic infrastructure and tools to fit Generalized Linear Models (GLMs) via penalized likelihood inference. Estimating procedures already implemented are the LQA algorithm (that is where its name come from), P-IRLS, RidgeBoost, GBlockBoost and ForwardBoost.

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Version

Install

install.packages('lqa')

Monthly Downloads

8

Version

1.0-3

License

GPL-2

Maintainer

Jan Ulbricht

Last Published

October 29th, 2012

Functions in lqa (1.0-3)

get.Amat

Computation of the approximated penalty matrix.
bridge

Bridge Penalty
predict.lqa

Prediction Method for lqa Fits
adaptive.lasso

Adaptive Lasso Penalty
cv.lqa

Finding Optimal Tuning Parameter via Cross-Validation or Validation Data
genet

Generalized Elastic Net Penalty
ao

Approximated Octagon Penalty
lqa-package

Fitting GLMs based on penalized likelihood inference.
cv.nng

Finding Optimal Tuning Parameter via Cross-Validation or Validation Data for non-negative garrote penalization
oscar

OSCAR Penalty
licb

L1-Norm based Improved Correlation-based Penalty
ridge

Ridge Penalty
lasso

Lasso Penalty
scad

The SCAD Penalty
penalreg

Correlation-based Penalty
lqa.control

Auxiliary for controlling lqa fitting
GBlockBoost

Computation of the GBlockBoost Algorithm or Componentwise Boosting
penalty

Penalty Objects
lqa-internal

Internal lqa functions
weighted.fusion

Weighted Fusion Penalty
enet

Elastic Net Penalty
lqa

Fitting penalized Generalized Linear Models with the LQA algorithm
plot.lqa

Coefficient build-ups for penalized GLMs
ForwardBoost

Computation of the ForwardBoost Algorithm
fused.lasso

Fused Lasso Penalty
icb

Improved Correlation-based Penalty