S original by F. Murtagh <fmurtagh@eso.org>.
R port by Kurt Hornik, Friedrich Leisch and Achim Zeileis
Multivariate Data Analysis Routines including hierarchical
clustering, PCA, Sammon mapping, correspondence analysis, ...

Penalized Estimation and Forecasting of Multiple Subject Vector
Autoregressive (VAR) Models
c(
person("Zachary","Fisher", email = "fish.zachary@gmail.com", role = c("aut", "cre")),
person("Younghoon", "Kim", role = "aut"),
person("Vladas", "Pipiras", role = "aut")
)
Functions for simulating, estimating and forecasting stationary Vector Autoregressive (VAR) models from multiple subjects using penalized regression methods (LASSO and adaptive LASSO) using accelerated proximal gradient descent algorithms (Fisher, Kim and Pipiras, Under Review).
Zachary Fisher [aut, cre],
Younghoon Kim [aut],
Vladas Pipiras [aut]

Penalized Estimation and Forecasting of Multiple Subject Vector
Autoregressive (multi-VAR) Models
Functions for simulating, estimating and forecasting stationary Vector Autoregressive (VAR) models for multiple subject data using the penalized multi-VAR framework in Fisher, Kim and Pipiras (2020) <arXiv:2007.05052>.

c(
person("Zachary","Fisher", email = "fish.zachary@gmail.com", role = c("aut", "cre")),
person("Younghoon", "Kim", role = "ctb"),
person("Vladas", "Pipiras", role = "ctb")
)
methods, stats, utils, MASS, Rcpp (>= 1.0.3), Matrix, ggplot2,
vars, reshape2
Zachary Fisher [aut, cre],
Younghoon Kim [ctb],
Vladas Pipiras [ctb]

Penalized Estimation of Multiple-Subject Vector Autoregressive
(multi-VAR) Models
methods, stats, utils, MASS, Rcpp (>= 1.0.3), Matrix, ggplot2,
vars, reshape2, glmnet

Measuring Multivariate Dependence Using Distance Multivariance
c(person("Björn", "Böttcher", email = "bjoern.boettcher@tu-dresden.de", role = c("aut", "cre")),person("Martin", "Keller-Ressel", email = "martin.keller-ressel@tu-dresden.de", role = "ctb"))
Distance multivariance is a measure of dependence which can be used to detect and quantify dependence structures. The necessary functions are implemented in this packages, and examples are given. For the theoretic background we refer to the forthcoming papers: B. Böttcher, M. Keller-Ressel, R.L. Schilling (2017) Detecting independence of random vectors I + II. Preprints.
Björn Böttcher [aut, cre],
Martin Keller-Ressel [ctb]