New: std() now works on integer matrices and numeric vectors
Internal: Lots of internal changes for cleaner, more reliable code
New version numbering system
ncvreg 3.11-1 (2019-02-26)2019-02-26
Fixed: Leave-one-out cross-validation now works correctly for logistic regression
Documentation: Added documentation (online) for local mfdr
Documentation: Fixed some broken links and typos
ncvreg 3.11-0 (2018-08-30)2018-09-13
Change: returnX now turned on by default if X < 100 Mb (used to be 10 Mb)
Change: summary.ncvreg now based solely on local mfdr
Change: Loss functions now consistently defined as deviance for all types of models
Change: R^2 now consistently uses the Cox-Snell definition for all types of models
Change: cv.ncvreg and cv.ncvsurv now return fold assignments
Fixed: Can now pass fold assignments to cv.ncvsurv
Documentation: Lots of updates
Documentation: vignette now html (used to be pdf)
Documentation: pkgdown website
ncvreg 3.10-0 (2018-04-17)2018-04-17
New: summary.ncvreg and summary.ncvsurv now report tables of inference for each feature based on local mFDRs
New: Option to specify fold assignments in cv.ncvsurv
New: CVSE now calculated for Cox models, with option of quick or bootstrap
Change: returnX now turned on by default if X < 10 Mb
Change: cv.ncvsurv now balances censoring across fold assignments
Change: All data sets now follow DataX, Datay convention
Deprecated: cv.ind argument to cv.ncvreg is now called fold
Portability: Fixed C99 flag
Internal: Fixed & v && C issue
ncvreg 3.9-1 (2017-04-26)2017-04-27
Change: Poission now returns linear predictors, like other families
Internal: Changing PROTECT/UNPROTECT to conform to new coding standards
ncvreg 3.9-0 (2017-03-16)2017-03-16
Deprecated: fir() is now called mfdr()
Change: mfdr for Cox and logistic models no longer use the simplistic approximation of 3.7-0. These calculations are much more accurate, but more computationally intensive, so these are carried out in C now.
Change: mfdr for Cox and logistic models requires the model matrix X now.
Internal: Registration of native routines
Fixed: std() wasn’t matching up column names if one column got dropped
ncvreg 3.8-0 (2017-01-06)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
ncvreg 3.7-1 (2016-12-23)2016-12-23
Fixed: Various fixes for fir function
Fixed: Bug with high dimensional (p > n) Cox models
ncvreg 3.7-0 (2016-12-13)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
ncvreg 3.6-0 (2016-06-13)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
ncvreg 3.5-2 (2016-04-09)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
ncvreg 3.5-1 (2016-02-07)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
ncvreg 3.5-0 (2015-10-27)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.
ncvreg 3.4-0 (2015-05-04)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.
ncvreg 3.3-0 (2015-03-18)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.
ncvreg 3.2-0 (2014-07-12)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
ncvreg 3.1-0 (2014-02-25)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)
ncvreg 3.0-0 (2014-02-06)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
ncvreg 2.7-0 (2013-12-16)2013-12-16
New: “vars” and “nvars” options to predict function.
Changed: Modified look of summary(cvfit) output.
Internal: Modified details of .Call interface.
ncvreg 2.6-0 (2013-10-03)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
ncvreg 2.5-0 (2013-03-16)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.
ncvreg 2.4-0 (2012-10-10)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)
ncvreg 2.3-2 (2011-05-16)2011-05-16
Documentation: Fixed formatting error in citation.
ncvreg 2.3-1 (2011-05-11)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
ncvreg 2.3 (2011-05-06)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)
ncvreg 2.2 (2011-04-25)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.