rLifting 1.0.0

New features

• Native irregular-grid support: pass a sorted t vector to denoise_signal_offline(), denoise_signal_causal(), or t_val per sample to the stream closure from new_wavelet_stream(). The predict step uses Lagrange interpolation conditioned on the actual sample positions — no separate regularisation required.
• Two new boundary extensions: local_linear (OLS extrapolation with configurable neighbourhood ll_k) and one_sided (renormalised filter at the edge), bringing the total to five alongside the existing symmetric, periodic, and zero.
• SureShrink threshold rule (threshold_method = "sure"): per-level Stein Unbiased Risk Estimator, complementing the existing universal (VisuShrink) rule.
• SCAD shrinkage (shrinkage = "scad"): Smoothly Clipped Absolute Deviation (Antoniadis & Fan, 2001), added alongside the existing hard, soft, and semisoft shrinkages.
• tune_alpha_beta(): automatic selection of the recursive threshold parameters α and β by minimising SURE over the signal.
• diagnose_wavelet(): verification suite for custom wavelets — perfect reconstruction, vanishing moments, orthogonality, compact support, and shift sensitivity.
• Seven benchmark datasets bundled: benchmark_rlifting, benchmark_wavethresh, benchmark_adlift, benchmark_nlt, benchmark_rlifting_irregular, benchmark_adlift_irregular, benchmark_nlt_irregular — each based on 1,000 Monte Carlo replications per configuration.
• Eight vignettes: introduction, thresholding and tuning, causal/stream, boundary modes, irregular grids, extensions, full cross-package benchmarks, and a real-world case study (BabyECG infant cardiac monitoring).

Deprecated

• The method argument in denoise_signal_offline() and denoise_signal_causal() is deprecated in favour of the separate threshold_method and shrinkage arguments. Old code continues to work via a backward-compatibility shim; method will be removed in a future version.

rLifting 0.9.0

• Initial release of the package.
• Implements high-performance Wavelet Lifting Transforms.
• Features:
  • Unified offline (batch) denoising via denoise_signal_offline().
  • Causal (sliding-window) filtering via denoise_signal_causal().
  • Sample-by-sample stream processing via new_wavelet_stream().
  • Adaptive recursive thresholding (universal rule, hard/soft/semisoft shrinkage).
  • Zero-allocation ‘C++’ core via ‘Rcpp’.
  • Six built-in wavelets: Haar, DB2, CDF 5/3, CDF 9/7, DD4, Lazy.
  • Three boundary extensions: symmetric, periodic, zero.