CRAFT 0.4.0
- Replaced the original
craft_autotune() with the
accelerated implementation. Staged screening, scoring-only backtests,
bounded caches, dynamic parallel scheduling, and deferred full fitting
are now available under this one name.
- Removed the separate
craft_autotune_fast() function and
export. Existing calls to that name should be changed to
craft_autotune().
- The returned class is
craft_autotune. To evaluate every
candidate at the final budget, set
stage_budgets = n_testing.
- Consolidated source, reference documentation, README, and vignette;
updated regression tests and the benchmark for the single public tuning
interface.
CRAFT 0.3.0
- Added
craft_autotune_fast() with scoring-only backtests
that skip forecast distributions and correlation diagnostics, then fit
selected models in full.
- Added staged candidate screening with stable seeds and reuse of
completed origins, plus a common backtest-origin grid across candidate
configurations.
- Added bounded caches for trajectory embeddings and deterministic
regime fits, including their SVD computations. Stochastic and plotting
regime calls bypass regime caching. Cache entries never cross search
calls or training prefixes.
- Added dynamically scheduled parallel scoring with one history
transfer per worker, compact scoring results, and no nested
parallelism.
- Added regression tests, usage documentation, and a reproducible
tuner benchmark.
- Preserved
craft_fit() and craft_autotune()
public interfaces.
CRAFT 0.2.0
- Added
craft_autotune() random-search hyperparameter
tuning for craft_fit().
- Added scalar, choice, integer, numeric, log-scale, and function
search spaces.
- Added backtest metric and composite ranking, retained top fits,
candidate failure diagnostics, and optional parallel search with bounded
worker counts.
- Added tuning examples, reference documentation, and regression
tests.
- Corrected stale license and maintainer information in the
README.
CRAFT 0.1.0
- Initial CRAN-preparation release.
- Added trajectory embedding helpers for lag and lead cumulative
changes.
- Added SVD changepoint regime detection and transition-based regime
forecasting through
craft_fit() and
craft_predict().
- Added smooth empirical forecast distribution fitting.
- Added tests, manual pages, README, and vignette.