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glmnet 4.1-2

A new feature added, as well as some minor fixes to documentation.

  • The exclude argument has come to life. Users can now pass a function that can take arguments x, y and weights, or a subset of these, for filtering variables. Details in documentation and vignette.
  • Prediction with single newx observation failed before. This is fixed.
  • Labeling of predictions from cv.glmnet improved.
  • Fixed a bug in mortran/fortran that caused program to loop ad infinitum

glmnet 4.1-1

Fixed some bugs in the coxpath function to do with sparse X.

  • when some penalty factors are zero, and X is sparse, we should not call GLM to get the start
  • apply does not work as intended with sparse X, so we now use matrix multiplies instead in computing lambda_max
  • added documentation for cv.glmnet to explain implications of supplying lambda

glmnet 4.1

Expanded scope for the Cox model.

  • We now allow (start, stop) data in addition to the original right-censored all start at zero option.
  • Allow for strata as in survival::coxph
  • Allow for sparse X matrix with Cox models (was not available before)
  • Provide method for survival::survfit

Vignettes are revised and reorganized. Additional index information stored on cv.glmnet objects, and included when printed.

glmnet 4.0-2

  • Biggest change. Cindex and auc calculations now use the concordance function from package survival
  • Minor changes. Allow coefficient warm starts for glmnet.fit. The print method for glmnet now really prints %Dev rather than the fraction.

glmnet 4.0

Major revision with added functionality. Any GLM family can be used now with glmnet, not just the built-in families. By passing a "family" object as the family argument (rather than a character string), one gets access to all families supported by glm. This development was programmed by our newest member of the glmnet team, Kenneth Tay.

glmnet 3.0-3

Bug fixes

  • Intercept=FALSE with "Gaussian" is fixed. The dev.ratio comes out correctly now. The mortran code was changed directly in 4 places. look for "standard". Thanks to Kenneth Tay.

glmnet 3.0-2

Bug fixes

  • confusion.glmnet was sometimes not returning a list because of apply collapsing structure
  • cv.mrelnet and cv.multnet dropping dimensions inappropriately
  • Fix to storePB to avoid segfault. Thanks Tomas Kalibera!
  • Changed the help for assess.glmnet and cousins to be more helpful!
  • Changed some logic in lambda.interp to avoid edge cases (thanks David Keplinger)

glmnet 3.0-1

Minor fix to correct Depends in the DESCRIPTION to R (>= 3.6.0)

glmnet 3.0

This is a major revision with much added functionality, listed roughly in order of importance. An additional vignette called relax is supplied to describe the usage.

  • relax argument added to glmnet. This causes the models in the path to be refit without regularization. The resulting object inherits from class glmnet, and has an additional component, itself a glmnet object, which is the relaxed fit.
  • relax argument to cv.glmnet. This allows selection from a mixture of the relaxed fit and the regular fit. The mixture is governed by an argument gamma with a default of 5 values between 0 and 1.
  • predict, coef and plot methods for relaxed and cv.relaxed objects.
  • print method for relaxed object, and new print methods for cv.glmnet and cv.relaxed objects.
  • A progress bar is provided via an additional argument trace.it=TRUE to glmnet and cv.glmnet. This can also be set for the session via glmnet.control.
  • Three new functions assess.glmnet, roc.glmnet and confusion.glmnet for displaying the performance of models.
  • makeX for building the x matrix for input to glmnet. Main functionality is one-hot-encoding of factor variables, treatment of NA and creating sparse inputs.
  • bigGlm for fitting the GLMs of glmnet unpenalized.

In addition to these new features, some of the code in glmnet has been tidied up, especially related to CV.

glmnet 2.0-20

  • Fixed a bug in internal function coxnet.deviance to do with input pred, as well as saturated loglike (missing) and weights
  • added a coxgrad function for computing the gradient

glmnet 2.0-19

  • Fixed a bug in coxnet to do with ties between death set and risk set

glmnet 2.0-18

  • Added an option alignment to cv.glmnet, for cases when wierd things happen

glmnet 2.0-17

  • Further fixes to mortran to get clean fortran; current mortran src is in inst/mortran

glmnet 2.0-16

  • Additional fixes to mortran; current mortran src is in inst/mortran
  • Mortran uses double precision, and variables are initialized to avoid -Wall warnings
  • cleaned up repeat code in CV by creating a utility function

glmnet 2.0-15

  • Fixed up the mortran so that generic fortran compiler can run without any configure

glmnet 2.0-13

  • Cleaned up some bugs to do with exact prediction
  • newoffset created problems all over - fixed these

glmnet 2.0-11

  • Added protection with exact=TRUE calls to coef and predict. See help file for more details

glmnet 2.0-10

  • Two iterations to fix to fix native fortran registration.

glmnet 2.0-8

  • included native registration of fortran

glmnet 2.0-7

  • constant y blows up elnet; error trap included
  • fixed lambda.interp which was returning NaN under degenerate circumstances.

glmnet 2.0-6

  • added some code to extract time and status gracefully from a Surv object

glmnet 2.0-3

  • changed the usage of predict and coef with exact=TRUE. The user is strongly encouraged to supply the original x and y values, as well as any other data such as weights that were used in the original fit.

glmnet 2.0-1

  • Major upgrade to CV; let each model use its own lambdas, then predict at original set.
  • fixed some minor bugs

glmnet 1.9-9

  • fixed subsetting bug in lognet when some weights are zero and x is sparse

glmnet 1.9-8

  • fixed bug in multivariate response model (uninitialized variable), leading to valgrind issues
  • fixed issue with multinomial response matrix and zeros
  • Added a link to a glmnet vignette

glmnet 1.9-6

  • fixed bug in predict.glmnet, predict.multnet and predict.coxnet, when s= argument is used with a vector of values. It was not doing the matrix multiply correctly
  • changed documentation of glmnet to explain logistic response matrix

glmnet 1.9-5

  • added parallel capabilities, and fixed some minor bugs

glmnet 1.9-3

  • added intercept option

glmnet 1.9-1

  • added upper and lower bounds for coefficients
  • added glmnet.control for setting systems parameters
  • fixed serious bug in coxnet

glmnet 1.8-5

  • added exact=TRUE option for prediction and coef functions

glmnet 1.8

  • Major new release
  • added mgaussian family for multivariate response
  • added grouped option for multinomial family

glmnet 1.7-4

  • nasty bug fixed in fortran - removed reference to dble
  • check class of newx and make dgCmatrix if sparse

glmnet 1.7-1

  • lognet added a classnames component to the object
  • predict.lognet(type="class") now returns a character vector/matrix

glmnet 1.6

  • predict.glmnet : fixed bug with type="nonzero"
  • glmnet: Now x can inherit from sparseMatrix rather than the very specific dgCMatrix, and this will trigger sparse mode for glmnet

glmnet 1.5

  • glmnet.Rd (lambda.min) : changed value to 0.01 if nobs < nvars, (lambda) added warnings to avoid single value, (lambda.min): renamed it lambda.min.ratio
  • glmnet (lambda.min) : changed value to 0.01 if nobs < nvars (HessianExact) : changed the sense (it was wrong), (lambda.min): renamed it lambda.min.ratio. This allows it to be called lambda.min in a call though
  • predict.cv.glmnet (new function) : makes predictions directly from the saved glmnet object on the cv object
  • coef.cv.glmnet (new function) : as above
  • predict.cv.glmnet.Rd : help functions for the above
  • cv.glmnet : insert drop(y) to avoid 1 column matrices; now include a glmnet.fit object for later predictions
  • nonzeroCoef : added a special case for a single variable in x; it was dying on this
  • deviance.glmnet : included
  • deviance.glmnet.Rd : included

glmnet 1.4

  • Note that this starts from version glmnet_1.4.