varGuidTS
0.1.13Variance-Guided Time-Series Modeling for Temporal Risk Detection
Overview
Fits balanced-panel autoregressive models with conditional heteroscedasticity for temporal risk detection. The main estimator combines autoregressive exogenous mean modeling with GARCH-X variance modeling, subject-specific baseline terms, shared population coefficients, and L1 penalization for high-dimensional covariates. The package returns conditional mean and variance estimates, coefficient summaries, simulations, and exceedance-based risk scores defined as estimated conditional threshold-exceedance probabilities. The implementation builds on the lasso of Tibshirani (1996) doi:10.1111/j.2517-6161.1996.tb02080.x, generalized autoregressive conditional heteroscedasticity of Bollerslev (1986) doi:10.1016/0304-4076(86)90063-1, and L1-regularized high-dimensional time-series modeling of Medeiros and Mendes (2016) doi:10.1016/j.jeconom.2015.10.011.
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Health
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-06-0812 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-05-297 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Code & Tests
- Cyclomatic complexity
- 2.0 median / 96 max
- Test cases
- 4 / 0.06 per code line
- Documented parameters
- 100%
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
26 2 exported
Complexity
8.5 avg / 96 max
Call network
26 nodes / 15 edges
Test coverage has not been measured for this package yet; nodes fall back to a neutral fill.
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Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
1 release. R releases are shown for context.
- 0.1.13Latest2026-05-28 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-05-28
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.1.0
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet