Skip to contents

Changelog

Source: NEWS.md

OptSurvCutR v0.10.0 (2026-06-17)

Documentation Updates

  • Updated package documentation and supporting materials for clarity and consistency.
  • Refreshed version references and release documentation.
  • Performed general project maintenance and housekeeping.

Note: This release contains no code changes, feature additions, bug fixes, or API modifications.

OptSurvCutR v0.9.9 (2026-06-12)

Major Engine Enhancements

  • Integer Index-Space Mapping: Replaced the continuous floating-point search space with a discrete, bounded integer lattice mapped directly to sorted unique data row indices. This fundamentally shifts the engine math from an infinite decimal space to a finite spectrum of actual observations, eliminating micro-decimal overfitting and stochastic seed drift.
  • Hybrid Execution Routing: Upgraded threshold discovery to dynamically select the most efficient search engine. The package now defaults to a deterministic, exhaustive method = "systematic" grid sweep for lower dimensional profiles (1 or 2 cuts) to guarantee global maximum discovery with zero performance lag. For higher dimensions (K≥3K \ge 3), it seamlessly transitions to the evolutionary genetic algorithm (rgenoud backend).
  • Automated Proportional Hazards Diagnostics: Integrated a native 2-tier Schoenfeld residuals validation layer inside the core S3 summary() methods. The package automatically flags models violating the Cox proportional hazards assumption, distinguishing between Tier 1 (Proportional, Stable) and Tier 2 (Time-Varying, Dynamic Risk) cohorts.
  • Strict Structural Headroom Guardrails: Upgraded nmin cell-floor checking to calculate total available degrees of freedom prior to loop execution. When discrete data density collapses or drops below necessary cell floors, the engine throws clean UX alert messages and skips impossible configurations to prevent fatal R session crashes.

📊 Algorithmic Scaling Protocol

The table below outlines how OptSurvCutR automatically routes execution and scales its internal tuning parameters (pop.size and max.generations) based on target complexity (KK Cuts) and data structure:

Target Complexity (KK Cuts) Execution Method Default Population Size (pop.size) Default Search Lifespan (max.generations) Optimisation Mechanics
K=1K = 1 Cut systematic N/A (Exhaustive Grid) N/A (Exhaustive Grid) Full 1D Coordinate Vector Sweep
K=2K = 2 Cuts systematic N/A (Exhaustive Grid) N/A (Exhaustive Grid) Full 2D Cross-Lattice Matrix Sweep
K=3K = 3 Cuts genetic 100 50 3D Index Hyper-Lattice Traversal
K=4K = 4 Cuts genetic 120 55 4D Hyper-Lattice Spatial Search
K=5K = 5 Cuts genetic 150 60 5D Multi-Epitope Surface Clustering
K=6K = 6 Cuts genetic 180 65 6D High-Dimensional Coordinate Scan
K=7K = 7 Cuts genetic 200 70 7D Ultra-Deep Hyper-Volume Optimization
K≥8K \ge 8 Cuts genetic 250 80 Complex Deep Lattice Cluster Optimisation
Low-Density Data (Discrete) systematic N/A (Auto-collapsed) N/A (Auto-collapsed) Rigid Quantile Step Filtering / Cell Floor Defence
Validation / Bootstrap Loop Context Snapped 10 (Streamlined default) 2 (Streamlined default) Accelerated Resampling Stability Assessment

OptSurvCutR 0.9.8 (2026-06-03)

New Core Features & Architecture

  • Optimal Cut Number Discovery (find_cutpoint_number): Introduced a new core function to mathematically determine the optimal number of cut-points (0 to 4) before running the genetic search. It supports model selection via Information Criteria (AIC, AICc, and BIC) to balance accuracy against complexity, preventing overfitting to sample-specific noise.
  • 4-Tier Validation Stability System: Completely overhauled the output of validate_cutpoint(). The package now automatically evaluates both the Precision (Maximum CI Width) and Validity (Interval Overlap) of bootstrapped thresholds, categorising them into four distinct tiers: OPTIMAL (Tier 1), DISTINCT (Tier 2), CAUTION (Tier 3), and UNSTABLE (Tier 4).

Stability Tier Definitions

Stability Tier Criteria Clinical Interpretation
Tier 1 (OPTIMAL) Narrow CI Width (< 30%) AND No Overlap High reliability; thresholds are highly consistent across different samples.
Tier 2 (DISTINCT) Zero Interval Overlap (Regardless of CI Width) The threshold value may vary, but the subpopulations remain fundamentally separate.
Tier 3 (CAUTION) Moderate CI Width (30%–60%) WITH Overlap Moderate instability; thresholds are likely real but sensitive to outliers.
Tier 4 (UNSTABLE) High CI Width (> 60%) WITH Overlap High instability; the model is likely overfitting to sample-specific noise.
  • Modernised Console UI (cli Integration): Replaced standard cat() and print() outputs with the cli package. The package now features a highly readable, colour-coded, text-wrapping console UI for summaries, diagnostics, and tiered alerts, safely bypassing base R’s 1,000-character warning limits.

Bare-Metal Low-Level Core Optimisation

  • Rcpp Integer-Assignment Factory (cpp_get_group_assignments): Migrated the deepest internal bottlenecks from high-level R loops to a native, compiled C++ backend via Rcpp. Instead of relying on R’s single-threaded vector copying, a high-speed C++ loop now evaluates continuous variables against candidate thresholds and constructs group partitions directly at the hardware memory layer.
  • Elimination of findInterval and as.factor Bottlenecks: Replaced the native R group-allocation pipeline within the exhaustive grid search (.get_stat) and the evolutionary objective function (.obj). The engine now bypasses R’s high-level object allocation layers entirely, cutting memory overhead significantly during heavy permutation loops and genetic generation iterations.
  • Vectorised tabulate() Constraint Evaluation: Replaced the expensive R table() and any(nlevels() < num_cuts) validation checks with fast C++ matrix array metrics and vectorised tabulate() calls. Subgroup sizes are now verified instantly using raw memory pointers before any survival formula is compiled, saving thousands of microsecond allocations per evaluation cycle.
  • Hardened S3 Attribute Anchoring: Implemented a protected S3 structural binding using the structure(..., class = "factor") paradigm to map the C++ integer vectors into formula-compliant categorical fields. This prevents R’s internal object-replacement methods from stripping away structural attributes when dropped into the data pipeline, ensuring seamless, crash-free interoperability with survival::coxph and survival::survdiff.

Algorithmic Optimisation & Tie-Handling (Sparse Data Data-Paths)

  • Defensive Boundary Safety Buffering (The “Tie-Wedge”): Introduced an internal 2-patient mathematical tolerance window (nmin - 2) into both the systematic and genetic validation layers. This architectural guardrail resolves a structural vulnerability in global optimisation routines when evaluating heavily tied or highly zero-skewed continuous vectors. When a large percentage of observations share identical values (at the lower limit of detection or zero baselines), rigid group-size partitioning constraints become mathematically impossible to fulfil. The introduction of this tolerance window prevents strict gridlock rejections (-Inf / NA), allowing the core optimisation loops to safely navigate around data ties and locate valid optimal solutions.
  • Fail-Safe Compilation Guardrails: Added an environment tracking verification step to identify compilation or loading failures in the compiled C++ shared libraries at runtime. If an outdated server or obscure OS distribution blocks the binary namespace, the package prints a clear warning and automatically triggers a graceful degradation path (use_cpp = FALSE), falling back to the transparent, native R loop structures without throwing a fatal crash.

Major Performance Improvements & Memory Protections

  • C-Level Interval Math: Replaced cut() with findInterval(..., left.open = TRUE) within the fallback mathematical engines. This eliminates expensive string manipulations, making systematic grid searches faster while perfectly preserving the mathematical boundaries of the groups.
  • Genetic Algorithm Memoisation: Implemented a hash-based evaluation cache (eval_cache) inside the genetic algorithm wrapper. The algorithm now remembers previously evaluated cut-points and bypasses the survival::coxph model entirely for redundant guesses, drastically cutting computation time on large datasets with many generations.
  • Pre-Allocation for Genetic Search: Shifted text manipulation and formula generation outside the genetic algorithm loop. The algorithm now uses a pre-allocated data.frame template, eliminating thousands of redundant memory allocations.
  • OS-Optimised Bootstrapping: Upgraded the parallel processing backend in validate_cutpoint(). On Unix-based systems (Mac/Linux), the package now dynamically switches to FORK clusters (shared memory) instead of PSOCK. This drops data-transfer overhead to near-zero and drastically speeds up bootstrap validation.
  • Environment Memory-Isolation via Explicit Binding: Overhauled the internal execution layers to pass explicitly extracted atomic vector streams instead of raw, nested evaluations. This isolates the parent R session environments from leaking into parallel background threads, cutting down cumulative memory inflation during high-core execution.

Advanced Visualisation & Reporting (New Features)

  • Unified S3 Plotting Router: Overhauled the plot() method (plot.find_cutpoint()). It now supports full ... argument passthrough to survminer functions for deep customisation, and includes a return_data = TRUE “escape hatch” to extract the raw, stratified plotting data.
  • Schoenfeld Diagnostic Plots & 2-Tier Alert: Added type = "diagnostic" to automatically evaluate the proportional hazards assumption via survival::cox.zph() and plot the residuals. The summary() function now includes a 2-Tier diagnostic alert to warn users if predictive power shifts significantly over time, gracefully handling singular matrix edge cases.
  • Dashboard View: Added type = "all" to generate a comprehensive, stacked composite plot (powered by patchwork) showing both the predictor distribution and the resulting survival outcome curve in a single clinical snapshot.
  • Interactive Web Widgets: Introduced optsurv_interactive(), a wrapper function that converts any static OptSurvCutR plot into an interactive HTML widget via plotly (ideal for Vignettes and RMarkdown).
  • Clinical Aesthetics: Implemented theme_optsurv() and defaulted to colourblind-safe palettes (e.g., “nejm”) to enforce a unified, publication-ready aesthetic across all outputs.

Bug Fixes & Edge Cases (find_cutpoint)

  • Dynamic Log-Likelihood Extraction: Fixed a bug where the p_value criterion would fail (or skip tests) when no covariates were provided. The null model log-likelihood is now extracted dynamically regardless of model length.
  • Degenerate Data Handling: Added a safeguard to check for zero-variance in the time column, ensuring the function safely returns NA rather than crashing on pathological datasets.
  • Dropped Coefficients: Added a check to safely return -Inf instead of NA if survival::coxph completely drops a coefficient (e.g., due to extreme collinearity) during a hazard_ratio search.

Structural Adjustments & Edge-Case Vulnerabilities (find_cutpoint_number)

  • Unadjusted Profile Likelihood Fallback Engine: Resolved a critical evolutionary calculation bottleneck inside .obj() when running unadjusted genetic searches (method = "genetic"). By adding an implicit zero-covariate structural detector, the engine now dynamically bypasses manual parameter init beta matrix arrays when no adjusters are present. This prevents survival::coxph(..., iter.max = 0) from crashing on bare categorical split factors, ensuring unadjusted model selection tables calculate 1 and 2 cuts perfectly.
  • Harden Nmin Evaluation Sequence: Re-ordered the parameter evaluation pipeline to calculate absolute patient sample splits (nmin_abs) prior to executing total data headroom matrix checks. This successfully stops proportional constraints (e.g., nmin = 0.25) from silently bypassing sample capacity filters.
  • Boundary Tie Headroom Margin: Built a 2-patient safety headroom filter into the information criteria data-capacity logic. This automatically intercepts brittle or impossible search spaces (such as requesting 3 cuts on 580 patients with a strict 25% allocation limit) and down-caps or exits gracefully before triggering genetic algorithm failures.

Stability & Parallel Processing (validate_cutpoint)

  • CRAN-Safe Core Detector: Built an environment check (Sys.getenv("_R_CHECK_LIMIT_CORES_")) into the core architecture. The package now detects when it is running on CRAN’s testing servers and automatically throttles itself to 2 cores to prevent rejection, while allowing real users to utilise maximum CPU power.
  • Parallel Variable-Name Scope Isolation: Resolved a critical parallel clustering crash (argument "predictor" is missing, with no default) that occurred inside .run_permutations() during high-throughput bootstrap resampling. The signature now maps explicit variable text strings alongside the split memory streams, ensuring independent worker processes (%dopar%) can parse formulas cleanly across separate CPU nodes.
  • Data-Stream Alignment Fix for Scoped Models: Fixed a dataset scope conflict (argument "data" is missing, with no default) inside the parallel foreach environment. By binding the structural data components directly within the function parameter signature rather than relying on global environmental evaluations, parallel worker environments successfully execute deep iterations across any operating system without variable scope dropouts.
  • Renamed Predictor Collision Handling: Fixed a tracking bug inside summary.find_cutpoint_number_result where the function printed "Unknown" for the main predictor variable under specific covariate-adjusted models. The S3 summary framework now tracks original column metadata across all renaming phases to provide pristine diagnostic dashboards for clinical research reporting.
  • Parallel Closure Fix (... arguments): Fixed a critical bug where running validate_cutpoint with n_cores > 1 would instantly fail if extra arguments (like max.generations or pop.size) were passed to the foreach loop.
  • Console Spam Prevention: Bootstrapping with replacement naturally creates tied survival times, which can trigger expected model convergence warnings. Parallel model fits are now wrapped in suppressWarnings() to prevent these benign warnings from flooding the user’s console and breaking the progress bar.
  • Namespace Safety: Refactored the parallel execution block to reliably call the exported find_cutpoint() wrapper. This guarantees that parallel worker nodes on any operating system can successfully locate the internal evaluation functions without namespace crashes.
  • S3 Summary Parameter Printing Fix: Resolved an unescaped string and operator evaluation syntax error within the cli::cli_bullets parameter tracking block inside summary.find_cutpoint_number_result. All named bullet outputs are now tightly mapped to explicitly bounded, single-quoted parameters ("*" =), guaranteeing clean console rendering.

OptSurvCutR 0.2.1 (2026-04-20)

CRAN Compliance & Quality of Life Improvements

This patch release addresses CRAN reviewer feedback and polishes the package’s console behaviour and visual branding.

  • Enhanced Console Control: Replaced all informational print() and cat() statements with message() in the core functions (find_cutpoint(), find_cutpoint_number(), and validate_cutpoint()). Users can now easily silence progress text by wrapping functions in suppressMessages().
  • Cleaned Return Behaviors: Removed forced print() calls from the end of the main S3 calculation functions. Results now return silently when assigned to a variable, while preserving formatted output when called directly.
  • DESCRIPTION File Formatting: Removed single quotes around function and package names in the DESCRIPTION file to satisfy CRAN automated parsers.
  • Official Branding: Added the official OptSurvCutR hex logo! The logo is now bundled within the package (man/figures/logo.png) and fully integrated into the GitHub README and pkgdown website configuration.

OptSurvCutR 0.2.0 (2026-04-09)

DOCUMENTATION & STANDARDS

  • Initial CRAN submission.
  • Updated the package structure (minor) to meet the submission requirement.

OptSurvCutR 0.1.9.3 (2026-03-22)

DOCUMENTATION & STANDARDS

Graphical Abstract: Added the new graphical abstract to the image/ folder for inclusion in the README.md. OptSurvCutR Graphical Abstract

  • Instruction Page (README.md): Updated the main package repository instructions.

COMMUNITY & OUTREACH

Presentations: Presented at FOSDEM 2026 in the Bioinformatics & Computational Biology DevRoom in Brussels, Belgium. View lightning talk details

Presentations: Presented at R!SK 2026 (February 18–19, 2026). This 100% online conference focuses on evaluating, measuring, and mitigating risk across diverse industries including healthcare, finance, and insurance. The event featured deep content sessions and live Q&A interactions. View presentation abstract

OptSurvCutR 0.1.9.2 (2025-11-25)

ROPENSCI REVIEW UPDATES (ITERATION 2)

BREAKING CHANGES

  • Renamed Functions: plot_optimization_curve() →\toplot_optimisation_curve() (British English) and plot_schoenfeld() →\toplot_cutpoint_residuals() (Namespace conflict).
  • Renamed Argument: maxiter is now max.generations in genetic algorithm functions to match rgenoud.

IMPROVEMENTS

  • Explicit Parameters: Exposed pop.size and max.generations as explicit arguments in find_cutpoint() and find_cutpoint_number().
  • Consistent Logic: All functions now use floor() for nmin calculation to ensure identical sample size handling.
  • Better Validation: validate_cutpoint() now accepts nmin as a proportion and issues an informative message when using the default 90% buffer (Standard G2.10).
  • Internal Cleanup: Removed export of helper operator %||%.

DOCUMENTATION & STANDARDS

  • British English: Standardised all functions and documentation (e.g., “optimise”, “summarise”).
  • Standards Compliance: Audited and updated all @srrstats tags; moved non-applicable standards to R/OptSurvCutR-package.R.
  • Testing: Added tests for new genetic parameters and updated snapshot tests for S3 methods.

OptSurvCutR 0.1.9 (2025-11-20)

REFACTORING & MAINTENANCE

  • Simplified Parallel Interface: The use_parallel argument was removed from validate_cutpoint(). Parallel execution is now controlled exclusively by the n_cores argument (n_cores = 1 for sequential, n_cores > 1 for parallel).
  • Consolidated Validation Logic: A new internal helper .validate_event_column() was created in R/utils-helpers.R to centralise the logic for checking that an event column is numeric and contains only 0s and 1s. Both find_cutpoint_number() and the internal helper .validate_data_conditions() (also in R/utils-helpers.R) were updated to call this function, eliminating duplicated code.
  • Simplified Parallel Code: In validate_cutpoint(), the brittle functions_to_export block was removed. The function now correctly relies on the .packages = "OptSurvCutR" argument in the foreach loop, making it easier to maintain.

BUG FIXES

  • Fixed Parallel Reproducibility: A bug in validate_cutpoint() that prevented true reproducibility for parallel runs (n_cores > 1) was fixed. The function now checks for and registers the doRNG package when a seed is provided, ensuring results are identical regardless of the number of cores used. The incorrect set.seed(i) call inside the foreach loop was removed.
  • Improved S3 Method Robustness: The summary.find_cutpoint_number_result() S3 method was updated to robustly handle NULL values in the object$parameters list (e.g., method = NULL). This prevents potential errors if a result object is created manually or improperly and aligns its behaviour with the print() method.

DOCUMENTATION

  • Clarified Log-Rank Test: The documentation for find_cutpoint() was updated to clarify that when covariates are provided, the "logrank" criterion is automatically generalised to the more appropriate Cox score test.

IMPROVEMENTS

  • Test Coverage: Increased test coverage to 88.5% with new unit tests for parallel reproducibility, S3 method edge cases, and internal validation helpers.
  • Planned: An rOpenSci submission is planned for review.
  • Planned: A JOSS submission is planned post-rOpenSci review.

OptSurvCutR 0.1.8

CRITICAL BUG FIXES

  • Parallel Validation: Fixed a critical bug where validate_cutpoint(use_parallel = TRUE) would fail. This was caused by helper functions not being exported to the parallel workers.
  • Event Column Validation: Fixed a major bug where non-numeric outcome_event data (e.g., "0:LIVING") caused silent failures. The functions now “fail-fast” with an error, validating the column is numeric with only 0s and 1s.
  • Genetic Algorithm Reporting: Fixed a bug where method = "genetic" would report a failure (-Inf) as a successful result. The functions now correctly check for the failure signal and return NA.

NEW FEATURES

  • G3.1a Compliance — Schoenfeld Residual Diagnostics: Added plot_diagnostics() to generate publication-ready Schoenfeld residual plots, assessing the proportional hazards (PH) assumption.

IMPROVEMENTS

  • Code Quality (Refactoring): Refactored find_cutpoint() to resolve “high cyclomatic complexity” NOTEs by moving validation logic to internal helper functions.
  • Test Coverage: Increased test coverage by adding new unit tests for error conditions, edge cases, and validation logic.
  • rOpenSci/pkgcheck Compliance:
    • Added examples to all exported functions.
    • Added @srrstats tags to satisfy multi-directory requirements.
  • R CMD check Compliance: Addressed all ERRORs, WARNINGs, and NOTEs:
    • Added inst/WORDLIST, _PACKAGE documentation, and fixed empty Rd sections.
    • Added codemeta.json to .Rbuildignore.
  • Code Style:
    • Reformatted code to follow Tidyverse Style Guide principles (e.g., <- for assignment, " for strings, consistent spacing around operators).
    • Strictly enforced an 80-character maximum line length for all code, comments, and documentation.
    • Made Roxygen documentation, inline comments, and user-facing messages more concise.
    • Replaced sapply() with type-safe vapply() in S3 methods to prevent potential bugs.
  • Internal Data: The crc_virome dataset’s status column was corrected to be numeric 0/1 to match documentation and new validation rules.

OptSurvCutR 0.1.7

NEW FEATURES

  • Covariate Adjustment Added: Both find_cutpoint() (for method = "systematic" and method = "genetic") and find_cutpoint_number() now support covariate adjustment via the covariates argument. This allows finding optimal cut-points and determining the optimal number of groups while accounting for potential confounders, providing a more robust assessment of a biomarker’s independent prognostic value.

IMPROVEMENTS

  • Major Performance Optimisation: Significantly optimised performance in find_cutpoint() for criterion = "hazard_ratio" and criterion = "p_value" (both systematic and genetic methods). This was achieved by removing computationally expensive summary() calls, extracting coefficients and statistics directly from model objects, and using a fast, manual Likelihood Ratio Test for p-value calculation.
  • Simplified Parallelism: Removed internal parallel processing (use_parallel argument) from find_cutpoint and find_cutpoint_number systematic search. This prevents potential issues with nested parallel calls and relies on standard external parallelisation approaches, such as the explicit parallel loop within validate_cutpoint.
  • Test Suite Overhaul: Revamped the entire testthat suite for improved reliability and robustness. Replaced brittle expect_snapshot() tests with more stable checks like expect_output(), expect_s3_class(), and specific value comparisons. Corrected logic for error and warning expectations and improved mocking for dependency checks. Code coverage increased significantly (for example, to ~86%).
  • User-Friendly rgenoud Check: Added clear, informative error messages using cli in find_cutpoint() and find_cutpoint_number() that trigger immediately if method = "genetic" is requested but the suggested rgenoud package is not installed, guiding the user on how to install it.
  • Dependency Management: Moved optional dependencies broom (for plotting) and withr (for testing) from Imports to Suggests in the DESCRIPTION file, making the core package installation lighter. Removed unused magrittr import.
  • Simplified Evidence Labels: Renamed evidence labels in find_cutpoint_number() results for brevity (for example, “Substantial support” -> “Substantial”).
  • Code Maintainability: Centralised all utils::globalVariables definitions into globals.R to resolve R CMD check NOTEs and improve clarity. Removed redundant code.

BUG FIXES

  • quiet = TRUE Message Fix: Fixed a critical bug where find_cutpoint() and find_cutpoint_number() would fail silently (no console message) when quiet = TRUE was set. Failure messages are now always printed to the console via internal helpers, regardless of the quiet setting, improving user feedback on errors.
  • Genetic Algorithm Edge Cases: Added more robust input validation and handling within the internal .run_genetic_search() function to gracefully manage edge cases like insufficient data variability or non-finite predictor ranges, preventing downstream errors and cryptic warnings.
  • Genetic Algorithm Monitor: Fixed the internal monitoring function used by the genetic algorithm (rgenoud::genoud) to correctly respect the print.level argument (controlled indirectly via user functions), ensuring progress updates are displayed or suppressed as intended.
  • NAMESPACE Fix (stats::): Resolved NAMESPACE errors and related test failures by adding explicit stats:: calls where needed (for example, for stats::quantile, stats::sd, stats::pchisq) and ensuring correct regeneration of the NAMESPACE file via devtools::document().
  • foreach NOTE Fix: Resolved R CMD check NOTE regarding “no visible binding for global”.

IMPROVEMENTS

  • Planned: An rOpenSci submission is planned for review.
  • Planned: A JOSS submission is planned post-rOpenSci review.

OptSurvCutR 0.1.6

NEW FEATURES

  • Added a vignette demonstrating the use of find_cutpoint() and validate_cutpoint() with TCGA virome data (for example, Alphapapillomavirus as a predictor), guiding users through cut-point optimisation and stability assessment for survival analysis.
  • Introduced comprehensive unit tests using testthat, covering core functions (find_cutpoint(), find_cutpoint_number(), validate_cutpoint()) and edge cases like missing data or small sample sizes, with code coverage reporting via covr to ensure reliability (>80% coverage).

IMPROVEMENTS

  • Optimised the genetic algorithm in find_cutpoint() by implementing adaptive pop.size (for example, 50 for num_cuts = 1) and max.generations (for example, 75), reducing runtime by 20–50% for survival datasets while maintaining accuracy for optimal cut-point selection.
  • Enhanced error messages in validate_cutpoint() to provide specific feedback on bootstrap validation failures, such as insufficient sample sizes or non-converging coxph models, improving user debugging experience.
  • Added a pkgdown GitHub Action to automatically build a package website, improving documentation accessibility, and updated README with badges for build status and code coverage to signal package reliability.
  • Updated DESCRIPTION with corrected URLs, dependency versions, and regenerated Rd files for consistent documentation across all functions.

BUG FIXES

  • Fixed NA handling in find_cutpoint() to robustly process survival datasets with missing predictor values, preventing errors in coxph or survdiff model fitting for real-world data like TCGA virome datasets.

OptSurvCutR 0.1.5

IMPROVEMENTS

  • Updated core functions (for example, find_cutpoint(), find_cutpoint_number()) with improved numerical stability and accuracy for survival model fitting, particularly for genetic algorithm convergence in high-dimensional predictors.

BUG FIXES

  • Fixed bugs in script handling and input validation, improving reliability for edge cases like small datasets or constant predictors in survival analysis.
  • Reverted prior bug fixes to prevent potential regressions, ensuring stable behaviour in validate_cutpoint() during bootstrap validation runs.

OptSurvCutR 0.1.0

NEW FEATURES

  • Initial release of OptSurvCutR for optimising cut-points in survival analysis.
  • Added find_cutpoint() to identify optimal cut-points for continuous predictors using systematic or genetic algorithms (via rgenoud) with log-rank, p-value, or hazard-ratio criteria.
  • Added find_cutpoint_number() to select the optimal number of cut-points using AIC, AICc, or BIC.
  • Added validate_cutpoint() for bootstrap-based stability assessment of cut-points.
  • Supports survival models via survival::coxph and survival::survdiff.
  • Includes example usage with simulated churn data, adaptable to TCGA virome datasets (for example, Alphapapillomavirus).