# 3.8-0 (2017-01-06)
* Change: max.iter now based on total number of iterations for entire path
* Fixed: Bug when fitting Cox model for single lambda
* Fixed: std no longer drops dimnames
# 3.7-1 (2016-12-23)
* Fixed: Various fixes for fir function
* Fixed: Bug with high dimensional (p > n) Cox models
# 3.7-0 (2016-12-13)
* New: fir extended to Cox and logistic regression
* New: summary function for ncvreg and ncvsurv objects
* Change: Convergence criterion now based on RMSD of linear predictors
* Change: Additional options and improvements to plot.fir
* Change: Better display of fir objects
* Internal: Improved efficiency for Cox models (linear predictor calculation
now occurs in C, not R)
* Internal: Reorganized testing suite
* Fixed: lamNames with single lambda passed
* Fixed: loss wasn't being returned for gaussian if failure to converge
* Fixed: perm.ncvreg would return NAs when models were saturated
# 3.6-0 (2016-06-13)
* New: Exports std() function for standardizing a design matrix
* Fixed: In predict.cv.ncvsurv
* Documentation: Added 'quick start' vignette
* Internal: Improved efficiency for cox models (avoids recalculating linear
predictors)
* Internal: Reorganized testing suite
* Internal: 'survival' package now used for setupLambda in Cox models
# 3.5-2 (2016-04-09)
* New: Added user interrupt checking
* Fixed: In ncvsurv with integer penalty factors
* Fixed: Rare numerical accuracy bug in cv fold assignments
* Fixed: LOOCV bug introduced by bias-correction feature
# 3.5-1 (2016-02-07)
* New: Compute bias correction for CV error; this is an experimental
feature at this point and may change in the future
* Internal: Replaced AUC function with more efficient version using
survival package
* Fixed: Penalty.factor for cv.ncvsurv when some columns may be degenerate
# 3.5-0 (2015-10-27)
* New: Added function AUC() to calculate cross-validated AUC values
for ncvsurv models.
* New: Option to return fitted values from cross-validation folds
(returnY=TRUE) for cv.ncvreg and cv.ncvsurv.
* Change: New method for calculation of cross-validation loss in cv.ncvsurv.
* Change: More accurate calculation for convexMin in the presence of
unpenalized variables
* Fixed: Factor-valued y with CV logistic regression
* Internal: Substantial efficiency improvements throughout for Cox models.
Coordinate descent redesigned to work in O(n) instead of O(n^2) operations,
and R code redesigned at various points to avoid the creation of any n x n
matrices when fitting and cross-validating Cox regression models.
* Internal: Better double/int type checking for penalty.factor
* Internal: Modifications to NAMESPACE for compatibility with R 3.3.
# 3.4-0 (2015-05-04)
* New: Expanded predict function for Cox models. predict.ncvsurv now
estimates subject-specific survival functions and medians.
* New: Plot method for survival curves.
* New: Option in perm.ncvreg to permute residuals for linear
regression
* New: permres function to estimate false inclusion rates based on
residuals at a specific value of lambda
* New: Some support for factors in X, y. It is still recommended that
users convert X to a numeric matrix prior to fitting in order to ensure that
predict() methods work properly, but ncvreg will now allow you to pass a
data frame with factors and handle things appropriately.
* Fixed: In predict.ncvsurv, when applied to models with saturation issues.
* Fixed: Small memory leak in ncvsurv.
# 3.3-0 (2015-03-18)
* New: Support for fitting survival models added (ncvsurv), along
with predict, plot, and cv.ncvsurv support functions. Currently, Cox models
are the only type of survival model implemented.
* New: Parallelization support for cv.ncvreg (with help from Grant
Brown)
* Fixed: In cv.ncvreg, when attempting to use leave-one-out cross-validation
(thank you to Cajo ter Braak for pointing this out)
* Removed: ncvreg_fit; it may return in a future version of the package.
# 3.2-0 (2014-07-12)
* New: Automatically coerces X to matrix and y to numeric if possible
* New: Made ncvreg_fit more user-friendly: user no longer has to specify
lambda, works with coef, predict, plot, etc.
* Changed: Modified order of arguments for predict so that 'type' comes
before 'lambda' and 'which'
* Fixed: Bug in convexMin when used with penalty.factor option
* Internal: Updated algorithm to 'hybrid' strong/active cycling
# 3.1-0 (2014-02-25)
* New: 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 (2014-02-06)
* New: 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: Bug in cv.ncvreg for user-specified lambda sequence
* Internal: Revised algorithms to incorporate targeted cycling based on strong
rules
* Internal: Moved standardization to C
* Internal: Moved calculation of lambda sequence to C
* Internal: As a result of the above three changes, ncvreg now runs much
faster for large p
# 2.7-0 (2013-12-16)
* New: "vars" and "nvars" options to predict function.
* Changed: Modified look of summary(cvfit) output.
* Internal: Modified details of .Call interface.
# 2.6-0 (2013-10-03)
* New: Introduction of function ncvreg_fit for programmers who want to access
the internal C routines of ncvreg, bypassing internal standardization and
processing
* New: Added vertical.line and col options to plot.cv.ncvreg
* Fixed: Bug in axis annotations with plot.cv.ncvreg when model is saturated
* Fixed: Deviance calculation; would return NaN if fitted probabilities of 0
or 1 occurred for binomial outcomes
* Fixed: NAMESPACE for coef.cv.ncvreg and predict.cv.ncvreg
* Internal: .Call now used instead of .C
# 2.5-0 (2013-03-16)
* New: 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)
* New: 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 (2012-10-10)
* New: penalty.factor option
* New: coef and predict methods now accept lambda as argument
* New: logLik method (which in turn allows AIC/BIC)
* Changed: cv.grpreg now returns full data fit as well as CV errors
* Fixed: Error in definition/calculation of cross-validation error and
standard error
* Fixed: Bug that arose if lambda was scalar (instead of a vector)
* Fixed: Bug in cv.ncvreg for linear regression -- cross-validation was being
carried out deterministically (Thank you to Brenton Kenkel for pointing this
out)
* Fixed: Intercept for logistic regression was not being calculated for
lamda=0
* Internal: standardization more efficient
* Internal: cdfit_ now returns loss (RSS for gaussian, deviance for binomial)
# 2.3-2 (2011-05-16)
* Documentation: Fixed formatting error in citation.
# 2.3-1 (2011-05-11)
* Changed: plot.ncvreg: Made the passing of arguments for plot.ncvreg more
flexible, so that user can pass options concerning both the plot and the
lines
* Changed: plot.ncvreg: Changed some of the default settings with respect to
color (hcl instead of hsv) and line width
# 2.3 (2011-05-06)
* Documentation: Updated documentation for cv.ncvreg.Rd, which no longer
agreed with the function usage (this was an oversight in the release of
version 2.2)
# 2.2 (2011-04-25)
* New: plot.cv.ncvreg for plotting cv.ncvreg objects
* Changed: Divorced cross-validation from fitting in cv.ncvreg. 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.