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.
|Date of publication||2012-10-29 08:59:07|
|Maintainer||Jan Ulbricht <firstname.lastname@example.org>|
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