NEWS

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tidyeof 0.1.0

• couple() and tune_cca() gain method = "pcr" for principal components regression — an OLS alternative to CCA that maps predictor PC amplitudes to predictand PC amplitudes. Regularization is the predictor truncation (k_pred); the CCA-style coupling k/k_cca is inert for PCR (a warning is emitted if passed). CCA-specific diagnostics (get_canonical_*) are not defined for PCR couplings.
• patterns() accepts multi-attribute stars objects for combined (multivariate) EOF analysis — e.g. joint temperature + precipitation downscaling with physically consistent predictions. Requires scale = TRUE.
• The whole stack is multivariate-aware: project_patterns(), reconstruct(), couple()/predict(), tune_eof(), tune_cca(), and plot methods. CV metrics gain per-variable columns plus a pooled score.
• Breaking change: EOF attributes in patterns$eofs are now named after the variable (previously "weight").
• Cells with non-finite standardized anomalies (climatological sd of 0) are now excluded like NA cells.

Initial release.

Core Features

• patterns() for EOF extraction from stars spatiotemporal data, with area weighting, standardization, varimax rotation, and monthly anomalization
• couple() and predict() for CCA-based statistical downscaling
• common_patterns() for joint EOF decomposition across multiple datasets
• tune_eof() and tune_cca() for cross-validated hyperparameter selection with contiguous temporal folds
• Scree plots with North et al. (1982) error bars and modified Rule N significance testing
• Teleconnection maps with FDR-corrected significance contours
• Full integration with stars, sf, and the tidyverse

Architecture

• S3 classes: patterns, coupled_patterns, common_patterns, cv_folds
• Plot, print, summary, and predict methods for all major classes
• Automatic IRLBA truncated SVD for large datasets
• Unit preservation throughout the pipeline via the units package