lqa v1.0-3

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by Jan Ulbricht

Penalized Likelihood Inference for GLMs

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.

Functions in lqa

Name Description
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
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Details

Type Package
Date 2010-07-12
License GPL-2
LazyLoad yes
Packaged 2012-10-29 08:59:07 UTC; ripley
Repository CRAN
Date/Publication 2012-10-29 08:59:07
Contributors Jan Ulbricht

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