Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions, including all commonly known distributions.  Also computed are the corresponding probability density function and cumulative distribution function. See Chan, Nadarajah and Afuecheta (2015) <doi:10.1080/03610918.2014.944658> for more details.

Error variance estimation in ultrahigh dimensional datasets with four different methods, viz. Refitted cross validation, k-fold refitted cross validation, Bootstrap-refitted cross validation, Ensemble method.

Fits variance-guided linear regression models that provide an
    alternative to ordinary least squares (OLS) for general linear-model
    design matrices, including ANOVA-style encodings. The methods use an
    iteratively reweighted least squares estimator or an iteratively reweighted
    lasso estimator and implement the global linear mean-variance model from
    the associated 2026 Statistics in Medicine article <doi:10.1002/sim.70632>.
    Under the assumptions in that
    paper, the estimator matches the homoscedastic baseline in population
    predictive quasi-risk when variance is constant and improves on it when the
    variance depends on covariates. The grouping-based nonlinear prediction
    extension from Section 3 is available in the development version on GitHub.

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>.