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lqa (version 1.0-2)

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

1.0-2

License

GPL-2

Maintainer

Jan Ulbricht

Last Published

April 18th, 2010

Functions in lqa (1.0-2)

bridge

Bridge Penalty
ridge

Ridge Penalty
ForwardBoost

Computation of the ForwardBoost Algorithm
enet

Elastic Net Penalty
fused.lasso

Fused Lasso Penalty
licb

L1-Norm based Improved Correlation-based Penalty
weighted.fusion

Weighted Fusion Penalty
lqa

Fitting penalized Generalized Linear Models with the LQA algorithm
get.Amat

Computation of the approximated penalty matrix.
lasso

Lasso Penalty
penalty

Penalty Objects
lqa-package

Fitting GLMs based on penalized likelihood inference.
GBlockBoost

Computation of the GBlockBoost Algorithm or Componentwise Boosting
lqa-internal

Internal lqa functions
lqa.control

Auxiliary for controlling lqa fitting
icb

Improved Correlation-based Penalty
cv.lqa

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

Approximated Octagon Penalty
genet

Generalized Elastic Net Penalty
penalreg

Correlation-based Penalty
adaptive.lasso

Adaptive Lasso Penalty
plot.lqa

Coefficient build-ups for penalized GLMs
oscar

OSCAR Penalty
scad

The SCAD Penalty
predict.lqa

Prediction Method for lqa Fits