3.2-0
* Updated algorithm to 'hybrid' strong/active cycling
* Automatically coerces X to matrix and y to numeric if possible
* Made ncvreg_fit more user-friendly: user no longer has to
specify lambda, works with coef, predict, plot, etc.
* Fixed bug in convexMin when used with penalty.factor option
* Modified order of arguments for predict so that 'type' comes
before 'lambda' and 'which'.
3.1-0
* Added support for Poisson regression
* Fixed bug in ncvreg_fit that could arise when fitting a model
without an intercept
* Fixed bug in cv.ncvreg with univariate regression (thank you to
Diego Franco Saldana for pointing this out)
3.0-0
* Revised internal algorithms to incorporate targeted cycling
based on strong rules
* Moved standardization to C
* Moved calculation of lambda sequence to C
* As a result of the above three changes, ncvreg now runs
much faster for large p
* Added fir(), perm.ncvreg(), and plot.fir() functions for the
purposes of estimating and displaying false inclusion rates;
these are likely to evolve over the next few months
* Fixed a bug in cv.ncvreg for user-specified lambda sequence
2.7-0
* Added "vars" and "nvars" options to predict function.
* Modified look of summary(cvfit) output.
* Modified internal details of .Call interface.
2.6-0
* Introduction of function ncvreg_fit for programmers who want to
access the internal C routines of ncvreg, bypassing internal
standardization and processing
* Internal restructuring: .Call now used instead of .C
* Bug fix for axis annotations with plot.cv.ncvreg when model is
saturated.
* Bug fix for deviance calculation; would return NaN if fitted
probabilities of 0 or 1 occurred for binomial outcomes.
* Bug fix in NAMESPACE for coef.cv.ncvreg and predict.cv.ncvreg
* Added vertical.line and col options to plot.cv.ncvreg
2.5-0
* Added options in plot.cv.ncvreg to plot estimates of r-squared,
signal-to-noise ratio, scale parameter, and prediction error in
addition to cross-validation error (deviance)
* Added summary method for cv.ncvreg which displays the above
information at lambda.min, the value of lambda minimizing the
cross-validation error.
* Fixed bug in cv.ncvreg with user-defined lambda values.
2.4-0
* Fixed error in definition/calculation of cross-validation
error and standard error
* Added penalty.factor option
* cv.grpreg: Now returns full data fit as well as CV errors
* coef and predict methods now accept lambda as argument
* Added logLik method (which in turn allows AIC/BIC)
* Fixed bug that arose if lambda was scalar (instead of a vector)
* cdfit_ now returns loss (RSS for gaussian, deviance for binomial)
* Fixed bug in cv.ncvreg for linear regression -- cross-validation
was being carried out deterministically
(Thank you to Brenton Kenkel for pointing this out)
* Internal change: standardization more efficient
* Fixed bug: Intercept for logistic regression was not being
calculated for lamda=0
2.3-2
* Fixed formatting error in citation.
2.3-1
* plot.ncvreg: Made the passing of arguments more flexible, so
that user can pass options concerning both the plot and the lines.
* plot.ncvreg: Changed some of the default settings with respect
to color (hcl instead of hsv) and line width.
2.3
* cv.ncvreg.Rd: Fixed the documentation, which no longer agreed
with the function usage. This was an oversight in the release of
version 2.2.
2.2
* cv.ncvreg: Divorced cross-validation from fitting. From a user
perspective, this increases flexibility, although obtaining the
model with CV-chosen regularization parameter now requires two
calls (to ncvreg and cv.ncvreg). The functions, however, are
logically separate and involve entirely separate methods.
* plot.cv.ncvreg: Developed a plotting method specific to
cv.ncvreg objects.