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ncvreg 3.15.0

• New: boot_ncvreg() function to obtain confidence intervals
• New: assign_fold() function to assign folds for CV
• Change: seed is no longer an argument to CV functions; use assign_fold() instead

ncvreg 3.14.3

• Internal: Now using R_Calloc for R_USE_STRICT_R_HEADERS compatibility

ncvreg 3.14.2

• Documentation: Lots of formatting fixes to the documentation

ncvreg 3.14.1

• Fixed: cv.ncvreg(), cv.ncvsurv() no longer affect seed in global environment if seed is specified

ncvreg 3.14.0

• New: residuals() method
• New: std() can now be applied to new data
• New: summary.ncvreg() now offers sort option; fixes #13
• Change: fir() deprecated
• Change: local_mfdr() allows user to specify sigma; also uses CV if called with cv object
• Fixed: Manual color palettes now recycled correctly; fixes #40; thank you to Logan Harris for pointing this out
• Fixed: mfdr now works for Poisson
• Documentation: Adding vignettes on other CV criteria, adaptive rescaling
• Documentation: References reformatted, URLs updated, DOIs added
• Internal: C code for binomial, poisson now unified under glm structure
• Internal: Now using roxygen2 for all documentation

ncvreg 3.13.0

• 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

ncvreg 3.12.0

• 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

ncvreg 3.11.2

• 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

• 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

• 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

• 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

ncvreg 3.9-1

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

ncvreg 3.9-0

• 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

• 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

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

ncvreg 3.7-0

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Documentation: Fixed formatting error in citation.

ncvreg 2.3-1

• 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

• 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

• 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.

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They may not be fully stable and should be used with caution. We make no claims about them.