The philosophy in the package is described in Stasny (1988) <doi:10.2307/1391558> and Gutierrez, A., Trujillo, L. & Silva, N. (2014), <ISSN:1492-0921> to estimate the gross flows under complex surveys using a Markov chain approach with non response.

Focuses on data collecting, analyzing and visualization in green finance and environmental 
  risk research and analysis. Main function includes environmental data collecting from 
  official websites such as MEP (Ministry of Environmental Protection of China, <https://www.mee.gov.cn>), water 
  related projects identification and environmental data visualization.

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>.

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>.

Variable selection for ultrahigh-dimensional ("large p small n") linear Gaussian models using a fiducial framework allowing to draw inference on the parameters. Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>.