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faux

It is useful to be able to simulate data with a specified structure. The faux package provides some functions to make this process easier. See the package website for more details.

Installation

You can install the released version of faux from CRAN with:

install.packages("faux")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("scienceverse/faux")

Please note that the ‘faux’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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Version

Install

install.packages('faux')

Monthly Downloads

6,007

Version

1.2.3

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/scienceverse/faux

Homepage

https://scienceverse.github.io/faux/

Maintainer

Lisa DeBruine

Last Published

October 1st, 2025

Functions in faux (1.2.3)

cormat_from_triangle

Make Correlation Matrix from Triangle

contr_code_poly

Polynomial code a factor

contr_code_treatment

Treatment code a factor

Set/get global faux options

Sum code a factor

Convert parameter

get_design_long

Get design from long data

faux: Simulation Functions.

Convert data from long to wide format

Check a Matrix is Positive Definite

Convert design to JSON

Get data columns

Get parameters from a data table

interactive_design

Set design interactively

Get Coefficients from Data

get_contrast_vals

Get contrast values

Likert density function

Attractiveness ratings of faces

Less scary green messages

Make a correlation matrix

Output a nested list in RMarkdown list format

Get Fréchet-Hoefding bounds

Simulate missing data

Multiple correlated distributions

Multiple correlated normal distributions

Convert normal to beta

fix_name_labels

Fix name labels

Make a normal vector correlated to existing vectors

sample_from_pop

Sample Parameters from Population Parameters

Convert normal to truncated normal

Attractiveness rating subset

Get distribution functions

Convert normal to gamma

Convert truncated normal to normal

Convert normal to binomial

Simulate data from design (internal)

Likert quantile function

Convert normal to uniform

print.psychds_codebook

Print Codebook Object

Simulate category joint distribution

print.nested_list

Print Nested List

Generate a cross-classified sample

Print Design List

Convert negative binomial to normal

Convert normal to normal

Convert uniform to normal

Make unique pairs of level names for correlations

Convert data from wide to long format

std_alpha2average_r

Standardized Alpha to Average R

Generate a mixed design from existing data

Likert distribution function

Convert gamma to normal

Convert normal to poisson

Check readline input

Simulate data from design

Random Likert distribution

Convert normal to likert

Convert normal to negative binomial

Limits on Missing Value for Positive Definite Matrix

Simulate an existing dataframe

Add random factors to a data structure

Add random effects to a data frame

Convert binomial to normal

Add between factors

Add a contrast to a data frame

Add within factors

Convert beta to normal

Recode a categorical column

average_r2tau_0

Average r to Random Intercept SD

contr_code_difference

Difference code a factor

check_mixed_design

Get random intercepts for subjects and items

Validates the specified design

contr_code_anova

Anova code a factor

codebook_interactive

Interactive Codebook

contr_code_helmert

Helmert code a factor

Convert r for NORTA

Create PsychDS Codebook from Data