Title: Accessing a Geometallurgical Database with R
Authors@R: c(person("K. Gerald", "van den Boogaart", role = c("aut", "cre"),
person("Stephan", "Matos Camacho", role = c("aut"),
Author: K. Gerald van den Boogaart [aut, cre], Stephan Matos Camacho [aut]
Depends: R(>= 2.1.0), DBI, RMySQL, foreach, methods, digest, shiny
Description: A template for a geometallurgical database and a fast and easy
interface for accessing it is provided in this package.
Copyright: (C) 2016 by Helmholtz Institute Freiberg for Resource

Title: Geostatistics for Compositional Analysis
person(given = "Helmholtz-Zentrum Dresden-Rossendorf", role = "cph"),
person(given = "Edith Cowan University", role = "cph")
Suggests: FNN, JADE, jointDiag, DescTools, knitr, rmarkdown (>= 2.3),
Imports: methods, gstat, compositions (>= 2.0), sp, boot, foreach,
Description: Support for geostatistical analysis of multivariate data,
in particular data with restrictions, e.g. positive amounts data,
compositional data, distributional data, microstructural data, etc.
It includes descriptive analysis and modelling for such data, both
from a two-point Gaussian perspective and multipoint perspective.
The methods mainly follow Tolosana-Delgado, Mueller and van den
Copyright: (C) 2020 by Helmholtz-Zentrum Dresden-Rossendorf and Edith
Cowan University; gsi.DS code by Hassan Talebi
Author: Raimon Tolosana-Delgado [aut] (<https://orcid.org/0000-0001-9847-0462>),

'closeup.R' 'data.R' 'exploratools.R' 'genDiag.R' 'geostats.R'

Author: Yi Zhao <zhaoyi1026@gmail.com>, Xi Luo <xi.rossi.luo@gmail.com>
Description: Performs Granger mediation analysis (GMA) for time series. This package includes a single level GMA model and a two-level GMA model, for time series with hierarchically nested structure. The single level GMA model for the time series of a single participant performs the causal mediation analysis which integrates the structural equation modeling and the Granger causality frameworks. A vector autoregressive model of order p is employed to account for the spatiotemporal dependencies in the data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under the two-level model, by leveraging the variabilities across participants, the parameters are identifiable and consistently estimated based on a full conditional likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2017), Granger Mediation Analysis of Multiple Time Series with an Application to fMRI, <arXiv:1709.05328> for details.