- New: Options ‘xtx’ and ‘r’ for ncvfit()
- Internal: cv.ncvreg() now uses less memory (returnX off)
- Internal: Better error handling if a matrix is supplied for y
- Fixed: AUC() now compatible with survival 3.2.10

- New: ncvfit(), a raw API to the ncvreg solver with full control over standardization, etc.
- Changed: ncvreg and ncvsurv now issue warning for non-pathwise usage
- Internal: Now using tinytest for unit testing
- Fixed: Memory leak in cox-dh; resolves #20

- New: std() now works on integer matrices and numeric vectors
- Internal: Lots of internal changes for cleaner, more reliable code
- New version numbering system

- 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

- 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

- 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 Data\(X, Data\)y convention
- Deprecated: cv.ind argument to cv.ncvreg is now called fold
- Portability: Fixed C99 flag
- Internal: Fixed & v && C issue

- Change: Poission now returns linear predictors, like other families
- Internal: Changing PROTECT/UNPROTECT to conform to new coding standards

- 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

- 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

- Fixed: Various fixes for fir function
- Fixed: Bug with high dimensional (p > n) Cox models

- 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

- 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

- 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

- 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

- 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.

- 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.

- 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.

- 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

- 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)

- 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

- New: “vars” and “nvars” options to predict function.
- Changed: Modified look of summary(cvfit) output.
- Internal: Modified details of .Call interface.

- 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

- 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.

- 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)

- Documentation: Fixed formatting error in citation.

- 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

- 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)

- 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.