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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.