Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> and Shih and Emura (2019) <doi:10.1007/s00362-016-0865-5> for details.

Fiducial framework to perform inference on the quantiles for a generalized Pareto distribution model and on the parameters of the Pareto exceedance distribution, assuming the exceedance threshold is a known or unknown parameter. Reference: Damian V. Wandler & Jan Hannig (2012) <doi:10.1007/s10687-011-0127-9>.

Simulation of the generalized fiducial distribution for
    normal linear mixed models with interval data. Fiducial inference is
    somehow similar to Bayesian inference, in the sense that it is based
    on a distribution that represents the uncertainty about the
    parameters, like the posterior distribution in Bayesian statistics. It
    does not require a prior distribution, and it yields results close to
    frequentist results. Reference: Cisewski and Hannig (2012)
    <doi:10.1214/12-AOS1030>.

Fiducial framework for the logistic regression model. The fiducial distribution of the parameters of the logistic regression is simulated, allowing to perform statistical inference on any parameter of interest. The algorithm is taken from Jessi Cisewski's PhD thesis: Jessi Cisewski (2012), "Generalized fiducial inference for mixed linear models".

Accurate and computationally efficient p-value calculation methods for a general family of Fisher type statistics (GFisher). The GFisher covers Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. It allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and degrees of freedom to a given data. The new p-value calculation methods are based on novel ideas of moment-ratio matching and joint-distribution approximation. The technical details can be found in Hong Zhang and Zheyang Wu (2020) <arXiv:2003.01286>.