Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge.

`grpreg`

is an R package for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, and prediction are also provided.

- To install the latest release version from CRAN:
`install.packages("grpreg")`

- To install the latest development version from GitHub:
`devtools::install_github("pbreheny/grpreg")`

- Change: Cross-validation now balances censoring across folds for survival models
- Fixed: Leave-one-out cross-validation now works correctly for logistic regression

- New: cv.grpsurv now calculates SE, with bootstrap option
- Change: R^2 now consistently uses the Cox-Snell definition for all types of models
- Change: Survival loss now uses deviance
- Change: cv.grpsurv now uses 'fold', not 'cv.ind', to declare assignments
- Fixed: cv.grpreg now correctly handles out-of-order groups for Poisson
- Fixed: cv.grpsurv now correctly standardizes out-of-order groups
- Fixed: grpreg no longer returns loss=NA with family='binomial' for some lambda values
- Internal: SSR-BEDPP optimization reinstated after bug fix
- Internal: C code for binom/pois combined into gdfit_glm, lcdfit_glm
- Documentation: Lots of updates
- Documentation: vignette now html (used to be pdf)
- Documentation: pkgdown website

- Fixed: Works with arbitrarily "messy" group structures now (constant columns, out of order groups, etc.) due to restructuring of standardization/ orthogonalization
- Internal: SSR-BEDPP rule turned off due to bug

- Internal: C code now uses || instead of |

- Fixed: Bug in applying screening rules with group lasso for linear regression with user-specified lambda sequence (thank you very much to Natasha Sahr for pointing this out)

- Fixed: Cross-validation no longer fails when constant columns are present (thank you to Matthew Rosenberg for pointing this out)
- Fixed: Cross-validation no longer fails when group.multiplier is specified

- New: Additional tests and support for coersion of various types with respect to both X and y
- Change: Convergence criterion now based on RMSD of linear predictors
- Change: 'Lung' and 'Birthwt' data sets now use factor representation of group, as character vectors are inherently ambiguous with respect to order
- Change: max.iter now based on total number of iterations for entire path
- Internal: 'X', 'group', and 'group.multiplier' now bundled together in an object called 'XG' to enforce agreement at all times
- Internal: new SSR-BEDPP feature screening rule for group lasso
- Internal: Registration of native routines
- Internal: Changing PROTECT/UNPROTECT to conform to new coding standards
- Fixed: The binding of X and G fixes several potential bugs, including Issue #12 (GitHub)

- Fixed bug involving mismatch between group.multiplier and group if group is given out of order.

- Fixed: memory allocation bug
- Deprecation: Re-introduced 'birthwt.grpreg' for backwards compatibility, but this is deprecated

- New: methods for survival analysis (Cox modeling): grpsurv, cv.grpsurv, AUC, predict.grpsurv
- New: option to return fitted values from cross-validation folds (returnY=TRUE) to cv.grpreg and cv.grpsurv
- New: Added user interrupt checking
- Change: Reformatted (and renamed) example data set 'Birthwt'; added example data set 'Lung' for survival
- Internal: Greatly expanded suite of tests; various bugs identified and fixed as a result
- Documentation: Added vignettes (a quick-start guide and a detailed description of available penalties)

- New: cv.grpreg now allows user to specify lambda (thanks to Vincent Arel-Bundock for suggesting this change)
- Fixed: bug for predict.grpreg(fit, type="nvars") or type="ngroups" when scalar lambda value is passed
- Documentation: Updated citations

- New: More flexible interface through the 'group' argument; groups may now be out of order, and may be named rather than only consecutive integers
- New: 'X' can now be a matrix of integers (previously this would result in the passing of an incompatible storage type to C)
- New: Additional error checks to prevent cryptic error messages
- Internal: modifications to convergence monitoring
- New: Added corrected AIC and extended BIC as options with select()
- Change: summary.cv.grpreg now describes multitask learning models more accurately
- Fixed: bug for multitask learning when number of outcomes = 2 (thank you to Aluma Dembo for pointing this out)
- Fixed: Cross-validation for multitask learning now respects the multivariate structure of the response matrix
- Fixed: bug in cv.grpreg when attempting to use leave-one-out cross-validation

- Fixed: More rigorous initialization at C level to prevent possible memory access problems
- Fixed: predict() for types 'vars', 'nvars', and 'ngroups' with multivariate outcomes
- Fixed: As a consequence of the above fix, summary(cvfit) now works for multivariate outcomes (thank you to Cajo ter Braak for pointing out that this was broken)

- New: support for Poisson regression
- Internal: .Call now used instead of .C
- Fixed: bug in cv.grpreg when attempting to use leave-one-out cross-validation (thank you to Cajo ter Braak for pointing this out)

- Internal: Various internal changes to make the package more efficient for large data sets

- New: group exponential lasso 'gel' method
- New: 'gmax' option
- New: 'nvars' and 'ngroups' options for predict
- Change: appearance of summary.cv.grpreg display

- New: options in plot.cv.grpreg to plot estimates of r-squared, signal-to-noise ratio, scale parameter, and prediction error in addition to cross-validation error (deviance)
- New: grpreg and cv.grpreg now allow matrix y to facilitation group penalized methods for seemingly unrelated regressions/multitask learning. This is something of a 'beta' release at this point, and will be developed and refined further in future releases.
- New: 'summary' method for cv.grpreg objects
- New: 'coef' and 'predict' methods for cv.grpreg objects
- Change: Brought gBridge up to date so that it now handles constant columns, etc. (see # grpreg 2.2-0)
- Fixed: bug in predict type='coefficients' when 'lambda' argument specified
- Fixed: bug in cv.grpreg with user-defined lambda values

- Internal: Switched to SVD-based orthogonalization to allow for linear dependency within groups

- Fixed: compilation error for 32-bit Windows
- Fixed: bug in calculation of binomial deviance when fitted probabilities are close to 0 or 1

- New: select now Now allows '...' options to be passed to logLik
- New: Added option to plot norm of each group, rather than individual coefficients
- New: 'vars', 'groups', and 'norm' options added to 'predict'
- Change: cv.grpreg now returns full data fit as well as CV errors; this allows cv.grpreg to handle constant columns and fixes some bugs
- Fixed: logLik no longer calculates (meaningless) log-likelihoods for saturated models (thank you to Xiaowei Ren for pointing this out)
- Fixed: bug for returning group when some groups were eliminated due to constant columns

- New: grpreg can now handle constant columns (they produce beta=0)
- Fixed: Bug involving orthogonalization with unpenalized groups
- Internal: restructuring of C code

- New: Group MCP, group SCAD methods added
- New: Added 'cv.grpreg' to facilitate cross-validation
- New: 'dfmax' option
- New: 'group.multiplier' option
- New: Allows specification of unpenalized groups
- Change: gBridge now divorced from grpreg and given separate function
- Internal: New algorithm for group lasso
- Internal: Extensive internal refactoring of code
- Internal: standardize and orthogonalize functions added
- Internal: Much more extensive and reproducible code testing

- New: grpreg now returns 'loss'
- New: Added logLik method
- Change: Syntax of 'select' modified (no longer requires X, y to be passed)
- Change: 'plot.grpreg' function more flexible
- Change: 'n.lambda' to 'nlambda' in grpreg
- Change: 'a' to 'gamma' for MCP tuning parameter
- Change: 'lambda2' to 'alpha'
- Removed: 'monitor' no longer an option in grpreg
- Removed: 'criteria' option for select
- Fixed: Bug in calculation of df for gLasso (grpreg.c)
- Documendation: Updated citation and contact information