B. Böttcher, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors: generalized distance covariance and Gaussian covariance. VMSTA, 2018, Vol. 5, No. 3, 353-383. <arXiv:1711.07778>.
B. Böttcher, M. Keller-Ressel, R.L. Schilling, Distance multivariance: New dependence measures for random vectors. <arXiv:1711.07775>.
B. Böttcher, Dependence Structures - Estimation and Visualization Using Distance Multivariance. <arXiv:1712.06532>.
G. Berschneider, B. Böttcher, On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance. <arXiv:1808.07280>.

B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using Distance Multivariance. <arXiv:1712.06532>.
Imports: igraph, graphics, stats, Rcpp, microbenchmark

and quantify dependence of arbitrarily many random vectors. The necessary functions are
implemented in this packages and examples are given. It includes: distance multivariance,
distance multicorrelation, dependence structure detection, tests of independence and
copula versions of distance multivariance based on the Monte Carlo empirical transform.
Detailed references are given in the package description, as starting point for the
B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using
the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020),

Depends: R(>= 2.10.0), emulator (>= 1.2-8), methods, utils, abind
Description: A multivariate generalization of the emulator package

Imports: utils, emulator (>= 1.2-15), mvtnorm, methods, mathjaxr
Description: A multivariate generalization of the emulator package.

Imports: utils, emulator (>= 1.2-13), mvtnorm, methods

Imports: utils, emulator (>= 1.2-15), mvtnorm, methods

Title: 'Explorable Multiverse' Data Analysis and Reports
person("Abhraneel", "Sarma", role = c("aut", "cre"), email = "abhraneel@u.northwestern.edu"),
person("Michael", "Moon", role = "aut", email = "moon@utstat.toronto.edu"),
person("Matthew", "Kay", role = "aut", email = "mjskay@northwestern.edu"),
person("Alex", "Kale", role = "ctb", email = "kalea@uw.edu"),
person("Nathan", "Taback", role = "ctb", email = "nathan.taback@utoronto.ca"),
person("Fanny", "Chevalier", role = "ctb", email = "fanny@dgp.toronto.edu"),
person("Jessica", "Hullman", role = "ctb", email = "jessica.hullman@gmail.com"),
person("Pierre", "Dragicevic", role = "ctb", email = "pierre.dragicevic@inria.fr"),
person("Yvonne", "Jansen", role = "ctb", email = "yvonne.jansen@sorbonne-universite.fr")
Description: Implement 'multiverse' style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016)
to show the robustness of statistical inference. 'Multiverse analysis' is a philosophy of
statistical reporting where paper authors report the outcomes of many different statistical
analyses in order to show how fragile or robust their findings are.
The 'multiverse' package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm>
allows users to concisely and flexibly implement 'multiverse-style'
analysis, which involve declaring alternate ways of performing an analysis step, in R and R Notebooks.
methods, tidyr (>= 1.0.0), tibble, magrittr, tidyselect,
formatR, collections, evaluate, rstudioapi, berryFunctions
Suggests: ggplot2 (>= 3.0.0), testthat (>= 2.1.0), highr, lubridate,
rmarkdown, covr, broom, boot, gganimate, gifski, forcats,
stringr, cowplot, tidybayes, png, stringi, modelr, styler
Packaged: 2021-05-31 02:13:46 UTC; abhraneel