Provides tools for constructing longitudinal decision paths,
    quantifying temporal drift, tracking subgroup disparity trajectories,
    and stress-testing longitudinal conclusions under hidden bias.
    Implements three signature metrics: the Drift Intensity Index (DII),
    which measures structural instability in transition dynamics using the
    Frobenius norm of consecutive transition matrix differences; the Bias
    Amplification Index (BAI), which quantifies whether group disparities
    widen or converge over time; and the Temporal Fragility Index (TFI),
    which estimates the minimum hidden-bias perturbation required to nullify
    a longitudinal trend conclusion. An interactive 'shiny' application
    supports exploratory analysis, visualization, and reproducible reporting.
    Methods are motivated by applications in educational and social science
    research, including the Early Childhood Longitudinal Study (ECLS).
    The DII is based on the Frobenius norm as described in Golub and Van Loan
    (2013, ISBN:9781421407944). The TFI extends the hidden-bias sensitivity
    framework of Rosenbaum (2002, ISBN:9781441912633). The BAI draws on
    disparity-trajectory methods discussed in Duncan and Murnane
    (2011, ISBN:9780871542731).

A method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) <doi:10.1016/j.jspi.2007.11.003>.

Robust parameter estimation and prediction of Gaussian stochastic 
 process emulators. It allows for robust parameter estimation and prediction using 
 Gaussian stochastic process emulator. It also implements the parallel partial 
 Gaussian stochastic process emulator for computer model with massive outputs 
 See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; 
 Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.