rvest, xml2, jsonlite, httr, stringi, V8, data.table, tidyr,
scatterpie, ggplot2, ggrepel, circlize, googlesheets4, gsheet

Focuses on data collecting and analyzing in green finance and environmental
risk research and analysis. Main function includes environmental data collecting from
official websites like MEP (Ministry of Environmental Protection of China, <http://www.mep.gov.cn>), water
related projects identification and environmental data visualization.
rvest, xml2, jsonlite, httr, stringi, V8

rvest, xml2, jsonlite, httr, stringi, V8, data.table

Focuses on data collecting and analyzing 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, <http://www.mep.gov.cn>), water
related projects identification and environmental data visualization.

Generalized Fiducial Inference for Extremes
c(
person("Stéphane", "Laurent", role = c("aut", "cre"),
email = "laurent_step@outlook.fr"),
person("Jan", "Hannig", role = "aut"),
person("Damian", "V. Wandler", role = "aut")
)
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>.
Rcpp (>= 1.0.5), parallel, doParallel, foreach, stats
Stéphane Laurent [aut, cre],
Jan Hannig [aut],
Damian V. Wandler [aut]

Generalized Fiducial Inference for Ultrahigh-Dimensional
Regression
person(given = "Stéphane",
family = "Laurent",
role = c("aut", "cre"),
email = "laurent_step@outlook.fr")
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>.

Generalized Fiducial Inference for Low-Dimensional Linear
Regression
Fiducial framework for linear regression models allowing normal, Student, Cauchy, or logistic error terms. Only low-dimensional models are possible, such as the simple linear regression model, the quadratic regression model, or the one-way ANOVA model with two or three factor levels. Reference: Hannig, Lai & Lee (2014) <doi:10.1016/j.csda.2013.03.003>.
Rcpp, lazyeval, stats, utils, arrangements, spatstat, EigenR,
data.table