Package: varGuidTS
Version: 0.1.13
Date: 2026-05-20
Title: Variance-Guided Time-Series Modeling for Temporal Risk Detection
Authors@R: c(person("Zihao", "Wang", role = "aut"),
  person("Min", "Lu", email = "luminwin@gmail.com", role = c("aut", "cre")))
Author: Zihao Wang [aut],
  Min Lu [aut, cre]
Maintainer: Min Lu <luminwin@gmail.com>
BugReports: https://github.com/zionwzz/variance-guided-risk-demo/issues
Depends: R (>= 4.1.0)
Imports: stats, glmnet
Suggests: testthat (>= 3.0.0)
Description: 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>.
License: MIT + file LICENSE
URL: https://github.com/zionwzz/variance-guided-risk-demo
Encoding: UTF-8
RoxygenNote: 7.3.2
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-05-20 18:10:00 UTC; minlu
Repository: CRAN
Date/Publication: 2026-05-28 11:10:02 UTC
