Author: Jelle Goeman, Rosa Meijer, Nimisha Chaturvedi
Depends: R (>= 2.10.0), survival, methods
of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a
supported regression models are linear, logistic and Poisson

Title: L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation
Description: Fitting possibly high dimensional penalized

Author: Jelle Goeman, Rosa Meijer, Nimisha Chaturvedi, Matthew Lueder

Packaged: Wed Aug 29 17:26:42 2007; jjgoeman

Depends: R (>= 2.10.0), survival (>= 2.42-4), methods

Packaged: Mon Sep  3 18:26:16 2007; jjgoeman

Packaged: Thu Sep  6 18:31:47 2007; jjgoeman

Packaged: Fri Sep  7 11:13:49 2007; jjgoeman

Packaged: Wed Sep 19 11:43:42 2007; jjgoeman

Title: Penalized classification using Fisher's linear discriminant
Description: Implements the penalized LDA proposal of "Witten and
Tibshirani (2011), Penalized classification using Fisher's
linear discriminant, to appear in Journal of the Royal

Title: Penalized Classification using Fisher's Linear Discriminant
Description: Implements the penalized LDA proposal of "Witten and Tibshirani (2011), Penalized classification using Fisher's linear discriminant, to appear in Journal of the Royal Statistical Society, Series B".

Title: Feature Selection SVM using penalty functions
Author: Natalia Becker, Wiebke Werft, Axel Benner
Description: This package provides feature selection SVM using penalty functions. The smoothly clipped absolute deviation (SCAD) and 'L1-norm' penalties are availible up to now. Other functions will be implemented in the near feature.
Packaged: Wed Jan 14 12:23:05 2009; becker

Title: Feature Selection SVM using Penalty Functions
Depends: e1071, MASS, corpcor, statmod, tgp, mlegp, lhs
Description: Provides feature selection SVM using penalty
functions. The smoothly clipped absolute deviation (SCAD),
'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
SCAD' (SCAD and 'L2-norm') penalties are available. The tuning
parameters can be found using either a fixed grid or a interval

Description: Support Vector Machine (SVM) classification with simultaneous feature selection using penalty
functions is implemented. The smoothly clipped absolute deviation (SCAD),