c(person("Alan", "Genz", role = "aut"),
person("Frank", "Bretz", role = "aut"),
person("Tetsuhisa", "Miwa", role = "aut"),
person("Xuefei", "Mi", role = "aut"),
person("Friedrich", "Leisch", role = "ctb"),
person("Fabian", "Scheipl", role = "ctb"),
person("Bjoern", "Bornkamp", role = "ctb", comment = c(ORCID = "0000-0002-6294-8185")),
person("Martin", "Maechler", role = "ctb", comment = c(ORCID = "0000-0002-8685-9910")),
person("Torsten", "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org", comment = c(ORCID = "0000-0001-8301-0471")))
Alan Genz [aut],
Frank Bretz [aut],
Tetsuhisa Miwa [aut],
Xuefei Mi [aut],
Friedrich Leisch [ctb],
Fabian Scheipl [ctb],
Bjoern Bornkamp [ctb] (<https://orcid.org/0000-0002-6294-8185>),
Martin Maechler [ctb] (<https://orcid.org/0000-0002-8685-9910>),
Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)

Multivariate Normal and T Distribution functions of (Dunnett,
1989)
Duane Currie <duane.currie@acadiau.ca> and Jianan Peng, using
code from (Dunnett, Appl Stats., 1989)
Computes multivariate student and multivariate normal
integrals, given a correlation matrix structure defined by a
vector bpd, s.t.  rho(i,j) = bpd(i) * bpd(j) (product
correlation structure)

A function for plotting multivariate time series data

splines, graphics, grDevices, stats, RColorBrewer
A function for plotting multivariate time series data.

R (>= 2.13.0), methods, splines, RColorBrewer

methods, MASS, rTensor, nnTensor, ccTensor, ica, igraph
For single tensor data, any matrix factorization method can be specified the matricised tensor in each dimension by Multi-way Component Analysis (MWCA). An originally extended MWCA is also implemented to specify and decompose multiple matrices and tensors simultaneously (CoupledMWCA). See the reference section of GitHub README.md <https://github.com/rikenbit/mwTensor>, for details of the methods.

methods, MASS, rTensor, nnTensor, ccTensor, iTensor, igraph

MWA package for causal inference in spatiotemporal event data
MWA grants insights into causal relationships in spatiotemporal event data.