We have uploaded to CRAN a new version of glmpath, a package

which fits the L1 regularization path for generalized linear models.

The revision includes:

- coxpath, a function for fitting the L1-regularization path for the Cox

ph model;

- bootstrap functions for analyzing sparse solutions;

- the ability to mix in L2 regularization along with L1 (elasticnet).

We have also completed a report that describes these methods in detail:

http://www-stat.stanford.edu/~hastie/Papers/glmpath.pdfThe lars package in R fits the entire piecewise-linear L1 regularization

path for

the lasso. The coefficient paths for L1 regularized glms, however,

are not piecewise linear.

glmpath uses convex optimization - in particular predictor-corrector

methods -

to fit the coefficient path at important junctions. These junctions

are at the "knots" in |beta|

where variables enter/leave the active set; i.e. nonzero/zero values.

Users can request greater resolution at a cost of more computation,

and compute values

on a fine grid between the knots.

The code is fast, and can handle largish problems efficiently.

it took just over 4 sec system cpu time to fit the logistic

regression path for

the "spam" data from UCI with 3065 training obs and 57 predictors.

For a microarray example with 5000 variables and 100 observations, 11

seconds cpu time.

Currently glmpath implements binomial, poisson and gaussian families.

The additional coxpath algorithm

extends the concepts to survival data via the L1-regularized Cox

proportional hazards model.

We have added bootstrap functions, which help make sense of

automatically chosen models (via AIC). Shake up the data,

and a different model might be chosen. The bootstrap is a natural way of

measuring the strength (significance) of variables,

and more.

Mixing in a small amount of L2 regularization brings a lot of stability

to the lasso; for example, without it the limiting

sequence for a logistic regression model with separable data is

undefined. We have added this option to

glmpath and coxpath

Mee Young Park and Trevor Hastie

Update details:

### glmpath 0.90 -> glmpath 0.91 ###

<Major changes>

1. Elastic net penalty added

2. Coxpath functions: coxpath, cv.coxpath, plot.coxpath, predict.coxpath

2. Bootstrap functions: bootstrap.path, plot.bootpath

<Minor changes>

glmpath:

actions[[1]] label

subtract 1 from every id number in actions

bshoot.threshold = 0.1 instead of 0.5

relax.lambda = 1e-8 instead of 1e-9

re-center the intercept in b.corrector/b.predictor

fix gaussian AIC/BIC

corrector1:

fix the first element of AIC/BIC

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