IsadoreNabi/SignalY: SignalY package

SignalY Signal Extraction from Panel Data via Bayesian Sparse Regression and Spectral Decomposition SignalY is a comprehensive R toolkit for extracting latent signals from panel data through multivariate time series analysis. It addresses three fundamental problems in applied econometrics and signal processing: which variables matter (column selection), what is the underlying structure (series decomposition), and what is the persistence regime (unit root characterization). Performance Benchmarks (Recovery Tests) Factor Structure (3 latent factors) Method: PCA / DFM Recovery metric: $r > 0.95$ with exact factor-count recovery. Sparse Variable Selection (5 of 50 variables) Method: Horseshoe regression Recovery metric: $\mathrm{F1} > 0.85$ and precision $> 0.90$. Logarithmic Trend Recovery Method: EMD Recovery metric: $r > 0.95$ relative to the true trend. Multi-scale Cycle Extraction Method: Wavelet decomposition (D3 + D4) Recovery metric: $r > 0.70$ relative to the true cycle. Stochastic Trend + AR(2) Cycle Method: HP-GC Bayesian filter Recovery metric: trend correlation $r > 0.90$ and cycle correlation $r > 0.50$. Stationarity Classification Method: Unit Root Battery Recovery metric: $4/4$ correct classifications on synthetic data. Key Features Horseshoe Regression Bayesian sparse selection via regularized Horseshoe priors (Piironen & Vehtari, 2017), including shrinkage-profile diagnostics. PCA with Bootstrap Significance Principal-component analysis with block-bootstrap confidence intervals and entropy-based topology analysis. Dynamic Factor Models Automatic factor selection via Bai & Ng (2002) information criteria combined with VAR dynamics. **Wavelet Decomposition** MODWT using Daubechies wavelets with multi-resolution variance analysis (Percival & Walden, 2000). Empirical Mode Decomposition Data-adaptive IMF extraction for non-stationary and non-linear signals (Huang et al., 1998). HP-GC Bayesian Filter Grant & Chan (2017) unobserved-components Hodrick–Prescott filtering with MCMC-estimated smoothing parameters. Unit Root Battery ADF, Phillips–Perron, KPSS, and ERS procedures with automated synthesis and persistence classification. Integrated Orchestrator `signal_analysis()` executes the full pipeline in a single call with automated interpretation. Architecture SignalY is organized around three analytical layers that can be used independently or chained together: ┌───────────────────────────────────────────────────────────────────────┐│ signal_analysis() ││ Master orchestrator with print/summary/plot │├──────────────────────┬──────────────────────┬─────────────────────────┤│ COLUMN │ SERIES │ PERSISTENCE ││ SELECTION │ DECOMPOSITION │ ANALYSIS ││ │ │ ││ fit_horseshoe │ filter_wavelet │ test_unit_root ││ pca_bootstrap │ filter_emd │ • ADF ││ estimate_dfm │ filter_hpgc │ • PP ││ │ filter_all │ • KPSS ││ │ │ • ERS ││ │ │ • Automated synthesis │├──────────────────────┴──────────────────────┴─────────────────────────┤│ UTILITIES ││ apply_to_columns · compute_entropy · iplot · interpolate_na │└───────────────────────────────────────────────────────────────────────┘ Typical Workflow Scenarios Scenario A — Column Selection Only: You have a high-dimensional predictor matrix and need to identify which variables are structurally relevant. Use fit_horseshoe() for sparse Bayesian selection, pca_bootstrap() for factor structure, or estimate_dfm() for dynamic factors. Scenario B — Series Decomposition Only: You have a univariate time series and need to extract its trend, cycle, and residual components. Use filter_wavelet(), filter_emd(), or filter_hpgc(). Scenario C — Unit Root Analysis Only: You need to characterize the persistence properties of a series. Use test_unit_root() for a comprehensive battery of tests with automated synthesis. Scenario D — Mixed Workflow: Select relevant variables → construct a composite signal → decompose it → test stationarity of extracted components. Use signal_analysis() to orchestrate the full pipeline, or chain the individual functions. Quick Example library(SignalY) # Prepare data data