lqa: 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.

Author
Jan Ulbricht
Date of publication
2012-10-29 08:59:07
Maintainer
Jan Ulbricht <jan.ulbricht@stat.uni-muenchen.de>
License
GPL-2
Version
1.0-3

View on CRAN

Man pages

adaptive.lasso
Adaptive Lasso Penalty
ao
Approximated Octagon Penalty
bridge
Bridge Penalty
cv.lqa
Finding Optimal Tuning Parameter via Cross-Validation or...
cv.nng
Finding Optimal Tuning Parameter via Cross-Validation or...
enet
Elastic Net Penalty
ForwardBoost
Computation of the ForwardBoost Algorithm
fused.lasso
Fused Lasso Penalty
GBlockBoost
Computation of the GBlockBoost Algorithm or Componentwise...
genet
Generalized Elastic Net Penalty
get.Amat
Computation of the approximated penalty matrix.
icb
Improved Correlation-based Penalty
lasso
Lasso Penalty
licb
L1-Norm based Improved Correlation-based Penalty
lqa
Fitting penalized Generalized Linear Models with the LQA...
lqa.control
Auxiliary for controlling lqa fitting
lqa-internal
Internal lqa functions
lqa-package
Fitting GLMs based on penalized likelihood inference.
oscar
OSCAR Penalty
penalreg
Correlation-based Penalty
penalty
Penalty Objects
plot.lqa
Coefficient build-ups for penalized GLMs
predict.lqa
Prediction Method for lqa Fits
ridge
Ridge Penalty
scad
The SCAD Penalty
weighted.fusion
Weighted Fusion Penalty

Files in this package

lqa
lqa/NAMESPACE
lqa/man
lqa/man/bridge.Rd
lqa/man/weighted.fusion.Rd
lqa/man/fused.lasso.Rd
lqa/man/lqa.control.Rd
lqa/man/adaptive.lasso.Rd
lqa/man/penalty.Rd
lqa/man/enet.Rd
lqa/man/GBlockBoost.Rd
lqa/man/plot.lqa.Rd
lqa/man/penalreg.Rd
lqa/man/lqa-package.Rd
lqa/man/cv.nng.Rd
lqa/man/ridge.Rd
lqa/man/icb.Rd
lqa/man/scad.Rd
lqa/man/cv.lqa.Rd
lqa/man/ForwardBoost.Rd
lqa/man/oscar.Rd
lqa/man/get.Amat.Rd
lqa/man/predict.lqa.Rd
lqa/man/lasso.Rd
lqa/man/licb.Rd
lqa/man/lqa-internal.Rd
lqa/man/ao.Rd
lqa/man/genet.Rd
lqa/man/lqa.Rd
lqa/inst
lqa/inst/doc
lqa/inst/doc/lqa_UserGuide.pdf
lqa/DESCRIPTION
lqa/MD5
lqa/R
lqa/R/licb.R
lqa/R/nnls2.R
lqa/R/fused.lasso.R
lqa/R/enet.R
lqa/R/genet.R
lqa/R/icb.R
lqa/R/print.pred.lqa.R
lqa/R/weighted.fusion.R
lqa/R/aic.loss.R
lqa/R/lqa-internal.R
lqa/R/lambda.check.R
lqa/R/predict.lqa.R
lqa/R/scad.R
lqa/R/cv.lqa.R
lqa/R/lqa.control.R
lqa/R/cv.nng.R
lqa/R/dev.loss.R
lqa/R/penalty.R
lqa/R/adaptive.lasso.R
lqa/R/ao.R
lqa/R/lqa.R
lqa/R/nng.update.R
lqa/R/oscar.R
lqa/R/ForwardBoost.R
lqa/R/lasso.R
lqa/R/GBlockBoost.R
lqa/R/print.summary.lqa.R
lqa/R/lqa.update2.R
lqa/R/summary.lqa.R
lqa/R/gcv.loss.R
lqa/R/squared.loss.R
lqa/R/bic.loss.R
lqa/R/bridge.R
lqa/R/print.cv.lqa.R
lqa/R/get.Amat.R
lqa/R/plot.lqa.R
lqa/R/lqa.formula.R
lqa/R/penalreg.R
lqa/R/ridge.R
lqa/R/nnls.R
lqa/R/print.penalty.R
lqa/R/lqa.default.R