Release v6.7.0

New features

• Added simulatePlpBenchmarkData() to generate semi-synthetic benchmark data
  from covariate profiles or fitted PLP models.
• Added outcome-limited split settings for large data sets where training should
  use a target number of outcome-positive rows (#396).
• Added Broken Adaptive Ridge logistic regression via
  setBrokenAdaptiveRidge(), including automatic ridge initialization and
  out-of-fold penalty selection (#673).
• Added lifecycle-style custom hyperparameter generators.
• Persisted model names in the results data model for downstream viewers.

Bug fixes

• Generated iterative hard thresholding CV predictions using fitted prior
  variances and restored intercept fitting.
• Ensured Cyclops CV refits honor thread, seed, tolerance, and iteration
  settings (#672).
• Validated simulation outcome covariates and added support for user-supplied
  PLP outcome models.
• Fixed scikit-learn SVC JSON loading to restore probability prediction,
  preserve sparse-model predictions, support dense models, and retain all-zero
  columns in sparse matrices (#682).
• Defaulted missing evaluation types in evaluatePlp().
• Corrected imputation messages for the number of missing features.
• Added the existing diagnostic_predictors.probast_id column to the results
  data model specification (#674).

Performance and maintenance

• Reduced hyperparameter database serialization overhead.
• Raised and tested minimum dependency versions and added an R 4.1.3 dependency
  floor check.