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SignalY (version 1.1.1)

Signal Extraction from Panel Data via Bayesian Sparse Regression and Spectral Decomposition

Description

Provides a comprehensive toolkit for extracting latent signals from panel data through multivariate time series analysis. Implements spectral decomposition methods including wavelet multiresolution analysis via maximal overlap discrete wavelet transform, Percival and Walden (2000) , empirical mode decomposition for non-stationary signals, Huang et al. (1998) , and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott filter, Grant and Chan (2017) . Features Bayesian variable selection through regularized Horseshoe priors, Piironen and Vehtari (2017) , for identifying structurally relevant predictors from high-dimensional candidate sets. Includes dynamic factor model estimation, principal component analysis with bootstrap significance testing, and automated technical interpretation of signal morphology and variance topology.

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Install

install.packages('SignalY')

Version

1.1.1

License

MIT + file LICENSE

Issues

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Maintainer

José Mauricio Gómez Julián

Last Published

February 4th, 2026

Functions in SignalY (1.1.1)

horseshoe

Regularized Horseshoe Regression for Variable Selection
interpret_adf

Interpret ADF Test Results
pca_bootstrap

Principal Component Analysis with Bootstrap Significance Testing
pca_dfm

Principal Component Analysis and Dynamic Factor Models
iplot

Interactive Plot for Signal Analysis
interpret_ers

Interpret ERS DF-GLS Results
interpret_pp

Interpret Phillips-Perron Results
interpret_ers_ptest

Interpret ERS P-test Results
interpolate_na

Linear Interpolation for Missing Values
select_by_credible_interval

Select Variables Based on Credible Intervals
interpret_kpss

Interpret KPSS Results
select_by_magnitude

Select Variables Based on Effect Magnitude
print.signal_analysis

Print Method for signal_analysis Objects
plot.signal_analysis

Plot Method for signal_analysis Objects
safe_divide

Safe Division with Zero Handling
posterior_predictive_check_horseshoe

Posterior Predictive Check for Horseshoe Model
procrustes_rotation

Procrustes Rotation for Bootstrap Alignment
process_horseshoe_results

Process Horseshoe Results
print_horseshoe_summary

Print Horseshoe Summary
unit_root

Unit Root and Stationarity Tests
select_by_shrinkage

Select Variables Based on Shrinkage
summary.signal_analysis

Summary Method for signal_analysis Objects
print_unitroot_results

Print Unit Root Results
test_unit_root

Comprehensive Unit Root Test Suite
signal_analysis

Comprehensive Signal Analysis for Panel Data
utilities

Utility Functions for SignalY
validate_input

Validate Input Data Structure
synthesize_unitroot_results

Synthesize Unit Root Results
compute_partial_r2

Compute Partial R-squared
compute_entropy

Compute Shannon Entropy
apply_to_columns

Apply Function to Matrix Columns
estimate_dfm

Dynamic Factor Model Estimation
fit_horseshoe

Fit Regularized Horseshoe Regression Model
filter_all

Apply Multiple Filters to a Series
block_bootstrap

Create Block Bootstrap Samples
compute_horseshoe_loo

Compute LOO-CV for Horseshoe Model
extract_mcmc_diagnostics

Extract MCMC Diagnostics
evaluate_fit_quality_internal

Evaluate Fit Quality
format_numeric

Format Numeric Values for Display
generate_interpretation

Generate Automated Technical Interpretation
check_stationarity

Check Stationarity of AR Coefficients
create_unitroot_summary

Create Summary Data Frame
SignalY-package

SignalY: Signal Extraction from Panel Data via Bayesian Sparse Regression and Spectral Decomposition
filter_wavelet

Wavelet Multiresolution Analysis Filter
get_horseshoe_stan_code

Get Stan Code for Regularized Horseshoe
filter_emd

Empirical Mode Decomposition Filter
filter_hpgc

Grant-Chan Embedded Hodrick-Prescott Filter
filters

Signal Filtering Methods for Trend Extraction