# Power Analysis for Moderation and Mediation

## Documentation for package ‘power4mome’ version 0.2.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

arg_x_from_power | Sample Size and Effect Size Determination
as.power4test_by_es | Power By Effect Sizes
as.power4test_by_n | Power By Sample Sizes
bz_helpers | Helpers for the Boos-and-Zhang (2000) Method
c.power4test_by_es | Power By Effect Sizes
c.power4test_by_n | Power By Sample Sizes
cut_patterns | Process Data by Creating Ordinal Variables
do_test | Do a Test on Each Replication
find_par_names | Test All Free Parameters
fit_model | Fit a Model to a List of Datasets
gen_boot | Generate Bootstrap Estimates
gen_mc | Generate Monte Carlo Estimates
missing_values | Process Data by Generating Missing Values
model_matrices_pop | Generate the Population Model
n_from_power | Sample Size and Effect Size Determination
n_region_from_power | Sample Size and Effect Size Determination
ordinal_variables | Process Data by Creating Ordinal Variables
pba_diagnosis | Sample Size and Effect Size Determination
plot.n_region_from_power | Plot The Results of 'x_from_power'
plot.power4test_by_es | Plot a Power Curve
plot.power4test_by_n | Plot a Power Curve
plot.power_curve | Plot a Power Curve
plot.q_power_mediation | All-in-One Power Estimation For Mediation Models
plot.x_from_power | Plot The Results of 'x_from_power'
pool_sim_data | Simulate Datasets Based on a Model
pop_es_yaml | Parse YAML-Stye Values For 'pop_es'
power4test | Estimate the Power of a Test
power4test_by_es | Power By Effect Sizes
power4test_by_n | Power By Sample Sizes
power_curve | Power Curve
predict.power_curve | Predict Method for a 'power_curve' Object
print.n_region_from_power | Sample Size and Effect Size Determination
print.power4test | Estimate the Power of a Test
print.power4test_by_es | Power By Effect Sizes
print.power4test_by_n | Power By Sample Sizes
print.power_curve | Power Curve
print.q_power_mediation | All-in-One Power Estimation For Mediation Models
print.rejection_rates_df | Rejection Rates
print.sim_data | Simulate Datasets Based on a Model
print.sim_out | Create a 'sim_out' Object
print.summary.n_region_from_power | Summarize 'x_from_power' Results
print.summary.x_from_power | Summarize 'x_from_power' Results
print.test_out_list | Summarize Test Results
print.test_summary | Summarize Test Results
print.test_summary_list | Summarize Test Results
print.x_from_power | Sample Size and Effect Size Determination
ptable_pop | Generate the Population Model
q_power_mediation | All-in-One Power Estimation For Mediation Models
q_power_mediation_parallel | All-in-One Power Estimation For Mediation Models
q_power_mediation_serial | All-in-One Power Estimation For Mediation Models
q_power_mediation_simple | All-in-One Power Estimation For Mediation Models
rbeta_rs | Random Variable From a Beta Distribution
rbeta_rs2 | Random Variable From a Beta Distribution (User Range)
rbinary_rs | Random Binary Variable
rejection_rates | Rejection Rates
rejection_rates.default | Rejection Rates
rejection_rates.n_region_from_power | Rejection Rates
rejection_rates.power4test | Rejection Rates
rejection_rates.power4test_by_es | Rejection Rates
rejection_rates.power4test_by_n | Rejection Rates
rejection_rates.q_power_mediation | Rejection Rates
rejection_rates.x_from_power | Rejection Rates
rexp_rs | Random Variable From an Exponential Distribution
rlnorm_rs | Random Variable From a Lognormal Distribution
rpgnorm_rs | Random Variable From a Generalized Normal Distribution
Rs_bz_supported | Helpers for the Boos-and-Zhang (2000) Method
rt_rs | Random Variable From a t Distribution
runif_rs | Random Variable From a Uniform Distribution
R_for_bz | Helpers for the Boos-and-Zhang (2000) Method
scale_scores | Process Data by Computing Scale Scores
sim_data | Simulate Datasets Based on a Model
sim_out | Create a 'sim_out' Object
summarize_tests | Summarize Test Results
summary.n_region_from_power | Summarize 'x_from_power' Results
summary.q_power_mediation | All-in-One Power Estimation For Mediation Models
summary.x_from_power | Summarize 'x_from_power' Results
test_cond_indirect | Test a Conditional Indirect Effect
test_cond_indirect_effects | Test Several Conditional Indirect Effects
test_group_equal | Test Group Constraints
test_index_of_mome | Test a Moderated Mediation Effect
test_indirect_effect | Test an Indirect Effect
test_k_indirect_effects | Test Several Indirect Effects
test_moderation | Test All Moderation Effects
test_parameters | Test All Free Parameters
x_from_power | Sample Size and Effect Size Determination