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: 2026-10-17 08:47:04 UTC; root
Repository: https://luminwin.r-universe.dev
Date/Publication: 2026-05-28 12:49:04 UTC
RemoteUrl: https://github.com/cran/varGuidTS
RemoteRef: HEAD
RemoteSha: ba159ccd2c7891bdbdd00b5a311687945df6c927