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.

Facilitates the extraction and organization of strand-specific genomic features from GFF3 files. In many species and variants, high quality genome annotations are not always available, necessitating de novo annotation using tools such as AUGUSTUS (Stanke et al., 2006; <doi:10.1093/nar/gkl200>). However, downstream processing of such annotations to obtain structured information, such as strand-wise gene locations, transcript regions, and associated protein identifiers—can be computationally intensive and complex.
    'GFFStrandLoc' provides a streamlined framework to parse GFF3 files and generate structured outputs containing strand-wise and region-wise genomic coordinates for each transcript, along with their associated protein information. Additionally, it enables users to define custom promoter lengths and extract corresponding promoter region coordinates for genes in a strand-aware manner. By simplifying post-annotation processing, it enhances the usability of de novo annotated genomic datasets for downstream analysis and interpretation.

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.

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>.