Power Analysis for Moderation and Mediation
Do a Test on Each Replication
Fit a Model to a List of Datasets
Generate Bootstrap Estimates
Generate Monte Carlo Estimates
Plot a Power Curve
Plot The Results of 'x_from_power'
Parse YAML-Stye Values For 'pop_es'
Power Curve
power4mome: Power Analysis for Moderation and Mediation
Power By Effect Sizes
Power By Sample Sizes
Estimate the Power of a Test
Predict Method for a 'power_curve' Object
Generate the Population Model
Random Variable From a Beta Distribution
Random Variable From a Beta Distribution (User Range)
Random Binary Variable
Rejection Rates
Random Variable From an Exponential Distribution
Random Variable From a Lognormal Distribution
Random Variable From a Generalized Normal Distribution
Random Variable From a t Distribution
Random Variable From a Uniform Distribution
Simulate Datasets Based on a Model
Create a 'sim_out' Object
Summarize Test Results
Summarize 'x_from_power' Results
Test Several Conditional Indirect Effects
Test a Conditional Indirect Effect
Test a Moderated Mediation Effect
Test an Indirect Effect
Test Several Indirect Effects
Test All Moderation Effects
Test All Free Parameters
Sample Size and Effect Size Determination
Power analysis and sample size determination for moderation, mediation, and moderated mediation in models fitted by structural equation modelling using the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02> or by multiple regression. The package 'manymome' by Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z> is used to specify the indirect paths or conditional indirect paths to be tested.