Imagine Your Data Before You Collect It

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Documentation for package ‘fabricatr’ version 1.0.2

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fabricatr-package fabricatr package
add_level Fabricate data
correlate Perform generation of a correlated random variable.
cross_levels Creates panel or cross-classified data
draw_binary Draw discrete variables including binary, binomial count, poisson count, ordered, and categorical
draw_binary_icc Draw binary data with fixed intra-cluster correlation.
draw_binomial Draw discrete variables including binary, binomial count, poisson count, ordered, and categorical
draw_categorical Draw discrete variables including binary, binomial count, poisson count, ordered, and categorical
draw_count Draw discrete variables including binary, binomial count, poisson count, ordered, and categorical
draw_discrete Draw discrete variables including binary, binomial count, poisson count, ordered, and categorical
draw_likert Recode a latent variable into a Likert response variable
draw_multivariate Draw multivariate random variables
draw_normal_icc Draw normal data with fixed intra-cluster correlation.
draw_ordered Draw discrete variables including binary, binomial count, poisson count, ordered, and categorical
draw_quantile Draw discrete variables including binary, binomial count, poisson count, ordered, and categorical
fabricate Fabricate data
fabricatr fabricatr package
join_using Helper function handling specification of which variables to join a cross-classified data on, and what kind of correlation structure needed. Correlation structures can only be provided if the underlying call is a 'link_levels()' call.
link_levels Creates panel or cross-classified data
modify_level Fabricate data
nest_level Fabricate data
potential_outcomes Build potential outcomes variables
recycle Expands data to a given length through recycling.
resample_data Resample data, including hierarchical data
reveal_outcomes Reveal outcomes
split_quantile Split data into quantile buckets (e.g. terciles, quartiles, quantiles, deciles).