Changes in version 0.1.1 (2026-10-31) Initial CRAN release. - Updated the simulation-study reference to the published journal DOI. - Clarified public method descriptions and documentation. - Simplified the README while retaining development smoke tests. Changes in version 0.1.0.9016 - Clarified the supported penalty scope of the inferential workflows. coxlasso_fit() continues to expose the glmnet elastic-net mixing parameter for fitting flexibility, while the inferential validation in selSurv currently concerns Lasso screening (alpha = 1). - Restricted coxlasso_debias() and the associated wild and Efron score-bootstrap backends to Cox-Lasso pilots with alpha = 1; non-Lasso pilots now fail explicitly rather than returning apparently validated debiased inference. - Kept split-sample, fixed-support model-based bootstrap, and procedure-level re-selection workflows computationally available for non-Lasso mixing parameters, but added explicit warnings that these settings have not been separately validated inferentially in selSurv. - Added a package workflow guide documenting inferential targets, bootstrap interfaces, penalty-parameter naming, penalty scope, and tie-handling distinctions. - Added focused regression tests for Lasso-only debiasing safeguards and non-Lasso warning behavior. Changes in version 0.1.0.9015 - Made Cox tie conventions explicit across the model-based bootstrap workflows: penalized Cox-Lasso selection uses the Breslow convention, ordinary post-selection Cox refits use Efron's method, and model-based event generation uses an explicit Breslow cumulative baseline hazard. - Added compatibility handling for installed glmnet versions that do not formally support cox.ties, avoiding silently ignored tie arguments while preserving the historical Breslow selection convention. - Made the Cox-Lasso cross-validation convention explicit as deviance loss with grouped cross-validation, while retaining CV-min, 10 folds, and Lasso mixing alpha = 1 as the general fitting defaults. - Updated the general re-selection defaults to conf_level = 0.95, seed = NULL, and standardize = TRUE. A randomly generated root seed is stored when seed = NULL, after which bootstrap-generation and CV streams remain deterministic and method-order invariant. - Retained rng_mode = "legacy_nki70" and its historical Breslow, random-stream, solver-tolerance, and standardization conventions solely for exact reproduction of the archived NKI70 analysis. - Revised coxlasso_split() to separate interval confidence through conf_level = 0.95 from Cox-Lasso mixing through penalty_alpha = 1. Historical alpha calls remain supported as a deprecated confidence-level bridge. - Kept the historical direct bootstrap backend defaults unchanged for compatibility; the unified coxlasso_boot() interface remains the recommended bootstrap entry point with B = 500, conf_level = 0.95, and seed = NULL. - Added regression tests for tie handling, Breslow baseline generation, cross-validation conventions, re-selection seed semantics, general-purpose defaults, and split-sample compatibility. Changes in version 0.1.0.9014 - Standardized categorical data-frame inputs to stable treatment coding: factors, ordered factors, character variables, and logical variables now contribute (k-1) design-matrix coordinates using the first level as the reference, independent of data-frame column order. - Ordered factors are treated as ordinary categorical predictors rather than using polynomial contrasts; logical predictors use FALSE as the reference level. Numeric and integer covariates remain numeric. - Character predictors are converted to unordered factors. Users who require a particular reference category should supply a factor with an explicit level order or use relevel() before fitting. - Selection and re-selection continue to operate on individual design-matrix coordinates, not on whole categorical variables as groups. Consequently, Lasso selection can depend on the chosen reference level. - Added explicit checks for constant categorical predictors, unsupported data-frame column classes, empty inputs, missing or non-finite values, invalid or duplicated design-column names, and generated dummy-name collisions. - Added design-coordinate checks so that post-selection refits and externally supplied original workflows cannot silently use reordered or differently coded covariates. - Expanded regression tests for factor, ordered-factor, character, logical, numeric, sparse-matrix, reference-level, contrast-option, input-boundary, and procedure-level re-selection behavior. Changes in version 0.1.0.9013 - Centralized covariate design-matrix normalization across the main Cox-Lasso workflow and procedure-level re-selection. - coxlasso_boot_reselect() now accepts data-frame covariates using the same model.matrix(~ . - 1) expansion already used by coxlasso_fit(), including factor and logical columns. - Added explicit rejection of missing and non-finite covariate values before model fitting. - Added regression tests for numeric, factor/logical data-frame, and sparse-matrix inputs. - Preserved the existing coding convention, numerical behavior, and archived NKI70 re-selection parity. Changes in version 0.1.0.9012 - Added an experimental procedure-level model-based bootstrap with Cox-Lasso re-selection through coxlasso_boot_reselect(). - Added fixed-support, fixed-penalty re-selection, bootstrap CV-min, CV-1SE, and AIC tuning variants. - Non-selected coordinates are recorded as zero; failed non-empty unpenalized Cox refits remain missing rather than being converted to zero. - Added method-level diagnostics for support overlap, model size, selection/refit success, tuning parameters, and coordinate-level re-selection frequencies. - Added method-order-invariant random streams for normal use. - Added rng_mode = "legacy_nki70" solely for reproducibility of the archived NKI70 analysis. The B=20 regression gate reproduces the archived support diagnostics, including the historical AIC refit failure pattern. - The existing coxlasso_boot(inference = "conditional") semantics remain fixed-support and are not broadened to include procedure-level re-selection. Changes in version 0.1.0.9011 - Integrated the August 2026 unified coxlasso_boot() API with the recovered v12 package source. - Added dispatch between debiased wild bootstrap and Efron's bootstrap and fixed-support model-based bootstrap. - Added compatibility with the established v12 selSurv_debias class. - Kept procedure-level re-selection outside the first unified bootstrap API; re-selection remains available through coxlasso_boot_parametric(). - Added validation/dispatch tests and development use-case documentation. - Archived the historical selSurv IWSM text as an unpublished draft and noted that the retained IWSM 2026 proceedings contribution was the PoSI work. - Removed local .RData/.Rhistory workspace artifacts from the source tree. Changes in version 0.1.0.9001 - Added user-facing coef and se fields to coxlasso_fit() and coxlasso_refit() outputs. - Added support-specific coef_support and se_support fields to coxlasso_refit(). selSurv 0.1.0.9002 - Added documentation for S3 print methods. - Rewrote workflow Rd usage/argument documentation. - Moved local smoke-test examples and implementation notes under inst/. - Reduced R CMD check warnings/notes caused by package documentation layout. selSurv 0.1.0.9003 - Fixed Rd code/documentation mismatches for coxlasso_wildboot() and coxlasso_efron_events(). - Added .Rbuildignore entries for stale top-level example/development files. - Shortened the coxlasso_fit() Rd usage block. selSurv 0.1.0.9004 - Added explicit roxygen usage block for coxlasso_fit() to remove Rd line-width NOTE. - Broke the coxlasso_fit() function signature across lines. - Removed empty inst/extdata from the source tree. selSurv 0.1.0.9005 - Removed the obsolete combined coxlasso_workflows.Rd file to avoid duplicate Rd aliases after roxygen generates individual workflow documentation. - Removed the manual coxlasso_fit.Rd file so roxygen can regenerate it from the explicit multi-line @usage block. selSurv 0.1.0.9006 - Added plot_selection_diagnostics() for base-R visualization of re-selection and zero-frequency diagnostics. - Added tests for diagnostic comparison and plotting helpers. selSurv 0.1.0.9007 - Improved plot_selection_diagnostics() layout; default legend is now a compact top legend and neutral greys are used by default. selSurv 0.1.0.9008 - Added a prettier default Cleveland-style dot plot to plot_selection_diagnostics(). - Kept grouped bars available via plot_type = "bar". - Improved plot readability for many variables and small RStudio plot panes. selSurv 0.1.0.9009 - Added a colorblind-friendly color palette to plot_selection_diagnostics(). - Added a robust legend_position = "side" default using a separate legend panel. - Kept grouped bars available and improved the color bar plot option. selSurv 0.1.0.9010 - Replaced the default side legend by a top legend panel in plot_selection_diagnostics(). - This avoids legend clipping in the RStudio zoom window and keeps the main plot area readable. Changes in version 0.1.0.9000 Initial research implementation. - Added Cox-Lasso fitting workflow. - Added support-restricted Cox refitting. - Added approximate debiasing workflow. - Added multiplier wild and event-level score bootstraps. - Added model-based bootstrap with fixed-support, fixed-lambda re-selection, and retuned re-selection modes. - Added split-sample workflow. - Added target and method summary helpers. - Added vignette explaining target definitions and workflow interpretation. Changes in version 0.1.0 Initial public GitHub release. - Provides Cox-Lasso screening, support-restricted Cox refitting, split-sample inference, approximately debiased coefficient estimates, and score-level and model-based bootstrap workflows. - Supports fixed-support inference and procedure-level re-selection, with explicit distinctions between the corresponding inferential targets. - Corrects the Cox score and observed information calculations for tied event times under the Breslow and Efron conventions. - Includes numerical regression tests against an independent Cox partial-likelihood reference implementation. - Documents the supported Lasso penalty scope and the conventions used for fitting, refitting, and bootstrap inference.