Displaying 20 results from an estimated 4000 matches similar to: "glmnet_1.6 uploaded to CRAN"
2010 Nov 04
0
glmnet_1.5 uploaded to CRAN
This is a new version of glmnet, that incorporates some bug fixes and
speedups.
* a new convergence criterion which which offers 10x or more speedups for
saturated fits (mainly effects logistic, Poisson and Cox)
* one can now predict directly from a cv.object - see the help files for cv.glmnet
and predict.cv.glmnet
* other new methods are deviance() for "glmnet" and coef() for
2005 Dec 13
3
Age of an object?
It would be nice to have a date stamp on an object.
In S/Splus this was always available, because objects were files.
I have looked around, but I presume this information is not available.
--------------------------------------------------------------------
Trevor Hastie hastie at stanford.edu
Professor, Department of Statistics, Stanford University
Phone:
2010 Apr 28
0
New package for ICA uploaded to CRA
I have uploaded a new package to CRAN called ProDenICA.
This fits ICA models directly via product-density estimation
of the source densities. This package was promised on page 567 in the
2nd edition of our book 'Elements of Statistical Learning'
(Hastie, Tibshirani and Friedman, 2009, Springer) . Apologies that it is so late.
The method fits each source density by a tilted gaussian
2010 Apr 28
0
New package for ICA uploaded to CRA
I have uploaded a new package to CRAN called ProDenICA.
This fits ICA models directly via product-density estimation
of the source densities. This package was promised on page 567 in the
2nd edition of our book 'Elements of Statistical Learning'
(Hastie, Tibshirani and Friedman, 2009, Springer) . Apologies that it is so late.
The method fits each source density by a tilted gaussian
2004 Jun 24
3
problem with model.matrix
This works:
> model.matrix(~I(pos>3),data=data.frame(pos=c(1:5)))
(Intercept) I(pos > 3)TRUE
1 1 0
2 1 0
3 1 0
4 1 1
5 1 1
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$"I(pos > 3)"
[1] "contr.treatment"
2010 Nov 19
0
glmnet_1.5.1 uploaded to CRAN
In glmnet_1.5 a poor default was set for the argument type which caused the program
to be very slow or even crash when nvar (p) is very large.
The argument type (now called type.gaussian) has two options,
"covariance" or "naive", and is used for the default family="gaussion" model (squared error loss).
When type.gaussian="covariance", all inner-products
2004 Jan 07
0
Statistical Learning and Datamining course based on R/Splus tools
Short course: Statistical Learning and Data Mining
Trevor Hastie and Robert Tibshirani, Stanford University
Sheraton Hotel
Palo Alto, CA
Feb 26-27, 2004
This two-day course gives a detailed overview of statistical models
for data mining, inference and prediction. With the rapid
developments in internet technology, genomics and other high-tech
industries, we rely increasingly more on data
2004 Jul 12
0
Statistical Learning and Data Mining Course
Short course: Statistical Learning and Data Mining
Trevor Hastie and Robert Tibshirani, Stanford University
Georgetown University Conference Center
Washington DC
September 20-21, 2004
This two-day course gives a detailed overview of statistical models
for data mining, inference and prediction. With the rapid
developments in internet technology, genomics and other high-tech
industries, we
2005 Jan 04
0
Statistical Learning and Data Mining Course
Short course: Statistical Learning and Data Mining
Trevor Hastie and Robert Tibshirani, Stanford University
Sheraton Hotel,
Palo Alto, California
February 24 & 25, 2005
This two-day course gives a detailed overview of statistical models
for data mining, inference and prediction. With the rapid
developments in internet technology, genomics and other high-tech
industries, we rely
2006 Mar 07
0
Statistical Learning and Datamining Course
Short course: Statistical Learning and Data Mining II:
tools for tall and wide data
Trevor Hastie and Robert Tibshirani, Stanford University
Sheraton Hotel,
Palo Alto, California,
April 3-4, 2006.
This two-day course gives a detailed overview of statistical models for
data mining, inference and prediction. With the rapid developments
in internet technology, genomics, financial
2006 Jan 14
0
Data Mining Course
Short course: Statistical Learning and Data Mining II:
tools for tall and wide data
Trevor Hastie and Robert Tibshirani, Stanford University
Sheraton Hotel,
Palo Alto, California,
April 3-4, 2006.
This two-day course gives a detailed overview of statistical models for
data mining, inference and prediction. With the rapid developments
in internet technology, genomics, financial
2003 Apr 30
0
Least Angle Regression packages for R
Least Angle Regression software: LARS
"Least Angle Regression" ("LAR") is a new model selection
algorithm; a useful and less greedy version of traditional
forward selection methods. LAR is described in detail in a paper
by Brad Efron, Trevor Hastie, Iain Johnstone and Rob Tibshirani,
soon to appear in the Annals of Statistics.
The paper, as well as R and Splus packages, are
2003 Apr 30
0
Least Angle Regression packages for R
Least Angle Regression software: LARS
"Least Angle Regression" ("LAR") is a new model selection
algorithm; a useful and less greedy version of traditional
forward selection methods. LAR is described in detail in a paper
by Brad Efron, Trevor Hastie, Iain Johnstone and Rob Tibshirani,
soon to appear in the Annals of Statistics.
The paper, as well as R and Splus packages, are
2008 Jun 02
0
New glmnet package on CRAN
glmnet is a package that fits the regularization path for linear, two-
and multi-class logistic regression
models with "elastic net" regularization (tunable mixture of L1 and L2
penalties).
glmnet uses pathwise coordinate descent, and is very fast.
Some of the features of glmnet:
* by default it computes the path at 100 uniformly spaced (on the log
scale) values of the
2008 Jun 02
0
New glmnet package on CRAN
glmnet is a package that fits the regularization path for linear, two-
and multi-class logistic regression
models with "elastic net" regularization (tunable mixture of L1 and L2
penalties).
glmnet uses pathwise coordinate descent, and is very fast.
Some of the features of glmnet:
* by default it computes the path at 100 uniformly spaced (on the log
scale) values of the
2010 Apr 04
0
Major glmnet upgrade on CRAN
glmnet_1.2 has been uploaded to CRAN.
This is a major upgrade, with the following additional features:
* poisson family, with dense or sparse x
* Cox proportional hazards family, for dense x
* wide range of cross-validation features. All models have several criteria for cross-validation.
These include deviance, mean absolute error, misclassification error and "auc" for logistic or
2010 Apr 04
0
Major glmnet upgrade on CRAN
glmnet_1.2 has been uploaded to CRAN.
This is a major upgrade, with the following additional features:
* poisson family, with dense or sparse x
* Cox proportional hazards family, for dense x
* wide range of cross-validation features. All models have several criteria for cross-validation.
These include deviance, mean absolute error, misclassification error and "auc" for logistic or
2005 Apr 06
0
Version 0.93 of GAM package on CRAN
I have posted an update to the GAM package. Note that this package
implements gam() as described
in the "White" S book (Statistical models in S). In particular, you can
fit models with lo() terms (local regression)
and/or s() terms (smoothing splines), mixed in, of course, with any
terms appropriate for glms.
A number of bugs in version 0.92 have been fixed; notably
1) some problems
2005 Apr 06
0
Version 0.93 of GAM package on CRAN
I have posted an update to the GAM package. Note that this package
implements gam() as described
in the "White" S book (Statistical models in S). In particular, you can
fit models with lo() terms (local regression)
and/or s() terms (smoothing splines), mixed in, of course, with any
terms appropriate for glms.
A number of bugs in version 0.92 have been fixed; notably
1) some problems
2005 Nov 28
0
glmpath: L1 regularization path for glms
We have uploaded to CRAN the first version of glmpath,
which fits the L1 regularization path for generalized linear models.
The lars package 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