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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("debruine/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

1,624

Version

1.2.2

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/debruine/faux

Homepage

https://debruine.github.io/faux/

Maintainer

Lisa DeBruine

Last Published

January 15th, 2025

Functions in faux (1.2.2)

contr_code_poly

Polynomial code a factor

average_r2tau_0

Average r to Random Intercept SD

codebook_interactive

Interactive Codebook

contr_code_difference

Difference code a factor

contr_code_helmert

Helmert code a factor

check_mixed_design

Get random intercepts for subjects and items

faux: Simulation Functions.

Sum code a factor

contr_code_anova

Anova code a factor

contr_code_treatment

Treatment code a factor

Convert parameter

Set/get global faux options

Validates the specified design

cormat_from_triangle

Make Correlation Matrix from Triangle

Create PsychDS Codebook from Data

Convert r for NORTA

Make a correlation matrix

Get distribution functions

get_design_long

Get design from long data

Convert normal to beta

Output a nested list in RMarkdown list format

Get Coefficients from Data

Attractiveness ratings of faces

get_contrast_vals

Get contrast values

Likert density function

Convert gamma to normal

Attractiveness rating subset

Convert negative binomial to normal

Simulate missing data

Get parameters from a data table

Less scary green messages

Get data columns

Convert normal to uniform

Convert normal to truncated normal

interactive_design

Set design interactively

Check a Matrix is Positive Definite

Convert normal to likert

Get Fréchet-Hoefding bounds

fix_name_labels

Fix name labels

Convert design to JSON

Convert normal to normal

Convert data from long to wide format

Convert normal to poisson

Likert distribution function

Print Design List

print.nested_list

Print Nested List

Multiple correlated distributions

Convert normal to gamma

Convert normal to binomial

Multiple correlated normal distributions

Generate a mixed design from existing data

Make a normal vector correlated to existing vectors

Simulate data from design (internal)

Limits on Missing Value for Positive Definite Matrix

std_alpha2average_r

Standardized Alpha to Average R

Convert truncated normal to normal

print.psychds_codebook

Print Codebook Object

Convert normal to negative binomial

sample_from_pop

Sample Parameters from Population Parameters

Check readline input

Random Likert distribution

Simulate category joint distribution

Make unique pairs of level names for correlations

Generate a cross-classified sample

Simulate data from design

Convert uniform to normal

Likert quantile function

Convert data from wide to long format

Simulate an existing dataframe

Recode a categorical column

Add within factors

Add random effects to a data frame

Convert beta to normal

Convert binomial to normal

Add between factors

Add random factors to a data structure

Add a contrast to a data frame