Partition a data frame across multiple worker
processes to provide simple multicore parallelism.
covr, knitr, lubridate, mgcv, nycflights13, rmarkdown,
testthat (>= 3.0.0), vroom

callr (>= 3.5.1), crayon, dplyr (>= 1.0.0), magrittr, qs (>=
0.24.1), R6, rlang, tibble, vctrs (>= 0.3.6), tidyselect
covr, knitr, lubridate, mgcv, nycflights13, rmarkdown,
testthat (>= 3.0.2), vroom, withr

Partition a data frame across multiple worker processes to
provide simple multicore parallelism.

Multivariate Functional Additive Mixed Models
person("Alexander", "Volkmann",
email = "alexandervolkmann8@gmail.com", role = c("aut", "cre"))
An implementation for multivariate functional additive mixed
models (multiFAMM), see Volkmann et al. (2021, <arXiv:2103.06606>). It builds on developed methods for univariate sparse
functional regression models and multivariate functional principal component
analysis. This package contains the function to run a multiFAMM and some
convenience functions useful when working with large models. An additional
package on GitHub contains more convenience functions to reproduce the
analyses of the corresponding paper
(<https://github.com/alexvolkmann/multifammPaper>).
data.table, funData, MFPCA (>= 1.3-2), mgcv, sparseFLMM (>
0.3.0), stats, zoo

Multiverse Analyses for Conditioning Data
c(person(given = "Angelos-Miltiadis",
family = "Krypotos",
role = c("aut", "cre", "cph"),
email = "amkrypotos@gmail.com"))
A suite of functions for performing analyses, based on a multiverse approach, for conditioning data. Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos <doi:10.31234/osf.io/2z6pd>.
Angelos-Miltiadis Krypotos [aut, cre, cph]

Dealing with Several Images of a Same Object Constituted of
Different Zones
Deals with several images of a same object, constituted of different zones. Each image constitutes a variable for a given pixel. The user can interactively select different zones of an image. Then, multivariate analysis (PCA) can be run in order to characterize the different selected zones, according to the different images. Hotelling (Hotelling, 1931, <doi:10.1214/aoms/1177732979>) and Srivastava (Srivastava, 2009, <doi:10.1016/j.jmva.2006.11.002>) tests can be run to detect multivariate differences between the zones.