faux package - RDocumentation

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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

1.2.1

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/debruine/faux

Maintainer

Lisa DeBruine

Last Published

April 20th, 2023

Functions in faux (1.2.1)

Create PsychDS Codebook from Data

contr_code_poly

Polynomial code a factor

contr_code_helmert

Helmert code a factor

Sum code a factor

contr_code_anova

Anova code a factor

check_mixed_design

Get random intercepts for subjects and items

contr_code_difference

Difference code a factor

Validates the specified design

Likert density function

codebook_interactive

Interactive Codebook

Convert r for NORTA

Make a correlation matrix

Set/get global faux options

faux: Simulation Functions.

Attractiveness ratings of faces

get_design_long

Get design from long data

Get parameters from a data table

Attractiveness rating subset

Convert gamma to normal

Convert normal to beta

Output a nested list in RMarkdown list format

Get Coefficients from Data

Less scary green messages

Simulate missing data

contr_code_treatment

Treatment code a factor

Get data columns

interactive_design

Set design interactively

Convert parameter

Convert normal to binomial

fix_name_labels

Fix name labels

Likert distribution function

Get Fréchet-Hoefding bounds

Check a Matrix is Positive Definite

Convert normal to gamma

Convert design to JSON

Convert normal to normal

Convert normal to truncated normal

Convert normal to likert

Simulate data from design (internal)

Likert quantile function

Convert normal to poisson

Convert normal to uniform

print.psychds_codebook

Print Codebook Object

print.nested_list

Print Nested List

Check readline input

sample_from_pop

Sample Parameters from Population Parameters

Random Likert distribution

Multiple correlated distributions

Make unique pairs of level names for correlations

Convert uniform to normal

cormat_from_triangle

Make Correlation Matrix from Triangle

Get distribution functions

Convert data from long to wide format

Simulate data from design

Generate a mixed design from existing data

Generate a cross-classified sample

Simulate category joint distribution

Print Design List

std_alpha2average_r

Standardized Alpha to Average R

Simulate an existing dataframe

Limits on Missing Value for Positive Definite Matrix

Convert truncated normal to normal

Multiple correlated normal distributions

Make a normal vector correlated to existing vectors

Convert data from wide to long format

Add between factors

Add random effects to a data frame

Add random factors to a data structure

Recode a categorical column

Add within factors

Convert beta to normal

average_r2tau_0

Average r to Random Intercept SD

Convert binomial to normal

Add a contrast to a data frame