Package: SignalY 1.1.2

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

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) <doi:10.1017/CBO9780511841040>, empirical mode decomposition for non-stationary signals, Huang et al. (1998) <doi:10.1098/rspa.1998.0193>, and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott filter, Grant and Chan (2017) <doi:10.1016/j.jedc.2016.12.007>. Features Bayesian variable selection through regularized Horseshoe priors, Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>, 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.

Authors:Jose Mauricio Gomez Julian [aut, cre]

SignalY_1.1.2.tar.gz
SignalY_1.1.2.zip(r-4.7-any)SignalY_1.1.2.zip(r-4.6-any)SignalY_1.1.2.zip(r-4.5-any)
SignalY_1.1.2.tgz(r-4.6-any)SignalY_1.1.2.tgz(r-4.5-any)
SignalY_1.1.2.tar.gz(r-4.7-any)SignalY_1.1.2.tar.gz(r-4.6-any)
SignalY_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SignalY/json (API)

# Install 'SignalY' in R:
install.packages('SignalY', repos = c('https://isadorenabi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/isadorenabi/signaly/issues

On CRAN:

Conda:

signal-processingtime-series

3.60 score 3 scripts 245 downloads 16 exports 14 dependencies

Last updated from:bd6b12bf95. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK152
source / vignettesOK207
linux-releaseOK144
macos-releaseOK130
macos-oldrelOK91
windows-develOK104
windows-releaseOK103
windows-oldrelOK96
wasm-releaseOK116

Exports:apply_to_columnscompute_entropyestimate_dfmfilter_allfilter_emdfilter_hpgcfilter_waveletfit_horseshoeiplotpca_bootstrapselect_by_credible_intervalselect_by_magnitudeselect_by_projectionselect_by_shrinkagesignal_analysistest_unit_root

Dependencies:dotCall64EMDfieldslatticelocfitmapsmultitapernlmeRColorBrewerRcppspamurcaviridisLitewaveslim

SignalY: Comprehensive Signal Extraction from Panel Data
Introduction | Installation | Quick Start | Methodological Framework | Epistemological Foundation | Method Selection Guidance | Spectral Decomposition | Wavelet Multi-Resolution Analysis | Empirical Mode Decomposition | Bayesian HP-GC Filter | Bayesian Variable Selection | Regularized Horseshoe Prior | Interpreting Shrinkage Factors | Dimensionality Reduction | PCA with Bootstrap | Dynamic Factor Models | Unit Root Testing | Test Battery | Interpretation Framework | The Master Function | Automated Interpretation | Best Practices | 1. Start with Exploration | 2. Choose Methods Thoughtfully | 3. Validate with Domain Knowledge | 4. Handle Diagnostics | Computational Considerations | Memory and Time | Parallelization | Memory Management | References
Rendered fromsignaly-introduction.Rmdusingknitr::rmarkdown

Last update: 2026-04-05
Started: 2026-04-05

SignalY: Technical Documentation
Abstract | Introduction and Epistemological Framework | Motivation | Conceptual Mapping | Spectral Decomposition Methods | Wavelet Multi-Resolution Analysis | Theoretical Foundation | MODWT Implementation | Filter Selection | Scale-Period Correspondence | Empirical Mode Decomposition | Sifting Algorithm | Reconstruction Property | Bayesian HP-GC Filter | State Space Formulation | Prior Configurations | Bayesian Sparse Regression: Regularized Horseshoe | Model Specification | Horseshoe Prior | Regularized Horseshoe | Shrinkage Factors | Effective Number of Non-Zeros | Global Shrinkage Calibration | Stan Implementation | Dimensionality Reduction | PCA with Bootstrap Significance | Standard PCA | Bootstrap Significance Testing | Entropy of Loadings | Dynamic Factor Models | Model | Factor Selection via Information Criteria | Unit Root Testing | Test Battery | ADF Test | ERS Tests | DF-GLS | Point Optimal Test | KPSS Test | Synthesis Algorithm | Master Function: signal_analysis() | Pipeline Architecture | Output Structure | Interpretation Framework | Signal Characteristics | Sparsity Assessment | Information Topology | Computational Considerations | Complexity Analysis | Parallelization | CRAN Compliance Notes | Soft Dependency: cmdstanr | Namespace Hygiene | References
Rendered fromtechnical_specs.Rmdusingknitr::rmarkdown

Last update: 2026-04-05
Started: 2026-04-05