cycleTrendR 0.3.0

Major new features

• Introduced universal time handling via the new argument dates_type.
• Full support for:
  • dates_type = "date" (daily data)
  • dates_type = "posix" (sub-daily wearable/physiological data)
  • dates_type = "numeric" (neuroscience, simulations, spike trains)
• Internal unified time index (timenum) for consistent modeling across formats.

Enhancements

• Automatic switching between STL, Lomb–Scargle, Fourier, LOESS, GAM, and GAMM.
• Fourier harmonics now operate in time units of timenum.
• Improved change-point detection compatible with all time formats.
• Updated spectral analysis pipeline for irregular and numeric time series.
• New vignette: cycleTrendR in practice.

Bug fixes

• Removed hard-coded assumptions about Date class.
• Fixed plotting issues related to time axis.
• Improved robustness of bootstrap confidence intervals.

Documentation

• Updated README with universal examples.
• Added new vignette demonstrating Date, POSIXct, and numeric workflows.

cycleTrendR 0.2.0

• Major improvements to documentation, imports, and CRAN compliance.
• Added full roxygen2 documentation for all parameters and return values.
• Improved NAMESPACE management with explicit @importFrom directives.
• Enhanced vignette stability and reduced computational load in examples.
• Achieved full CRAN compliance: 0 errors, 0 warnings, 0 notes.

cycleTrendR 0.1.0

• Initial release of cycleTrendR.
• Added the main function adaptive_cycle_trend_analysis() supporting:
  • LOESS, GAM, and GAMM trend estimation
  • Automatic Fourier harmonic selection (AICc/BIC)
  • Lomb–Scargle periodogram for irregular sampling
  • Bootstrap confidence intervals (IID and MBB)
  • Change-point detection
  • Rolling-origin forecasting
• Added publication-quality ggplot2 visualizations:
  • Trend + CI
  • Periodogram
  • Residual ACF
  • Diagnostics and summary tables
• Added a comprehensive vignette: cycleTrendR-overview.
• Added README with installation instructions and examples.