Feature Annealed Independence Rules (demo)
Use FAIR to do classification on the high dimensional data

Allows user to obtain subsets of columns of data or vectors within a list.  These subsets will match the original data in terms of average and variation, but have a consistent length of data per column.  It is intended for use on automated data generation which may not always output the same N per replicate or sample.

Faisal Conjoint Model: A New Approach to Conjoint Analysis
Faisal Afzal Siddiqui, Ghulam Hussain, and Mudassiruddin
It is used for systematic analysis of decisions based on
attributes and its levels.

Faisal Afzal Siddiqui, Ghulam Hussain, and Mudassir Uddin

It is used for systematic analysis of decisions based on attributes and its levels.

Flexible Data Simulation Using the Multivariate Normal
Distribution
Simulation of data from Gaussian Graphical Models (B Bodinier, S Filippi, TH Nost, J Chiquet, M Chadeau-Hyam (2021) <arXiv:2106.02521>). By controlling the conditional independence structure between the variables, these multivariate simulation tools can be used to evaluate the performance of regression, dimensionality reduction or graphical models.
huge, igraph, MASS, Rdpack, withr (>= 2.4.0)

Simulates Data from a Data Frame of Different Variable Types
c(person("Lily", "Zhang", email = "lilyhzhang1029@gmail.com",
role = c("aut", "cre")),
person("Dustin","Tingley", email = "dtingley@gov.harvard.edu",
role = c("aut")))
Generates fake data from a dataset of different variable types.
The package contains the functions simulate_dataset and simulate_dataset_ts
to simulate time-independent and time-dependent data. It randomly samples
character and factor variables from contingency tables and  numeric and
ordered factors from a multivariate normal distribution. It currently supports the
simulation of stationary and zero-inflated count time series.
Lily Zhang [aut, cre],
Dustin Tingley [aut]

person(given = "Andreas Dominik", family = "Cullmann",
email = "fvafrcu@arcor.de", role = c("aut", "cre"))
Use R as a minimal build system. This might come in handy
if you are developing R packages and can not use a proper build
system. Stay away if you can (use a proper build system).
knitr, rmarkdown, testthat, RUnit, devtools, rprojroot,
roxygen2, hunspell, cleanr, lintr, covr
MakefileR, callr, withr, utils, igraph, graphics