R: Simulation-Based Power Analysis for Mixed-Effects Models

# Simulation-Based Power Analysis for Mixed-Effects Models

## Documentation for package ‘mixpower’ version 1.1.1

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

## Help Pages

mixpower-package | Simulation-Based Power Analysis for Mixed-Effects Models
as_tibble.mp_power | Coerce mixpower results to a tibble
autoplot.mp_sensitivity | ggplot2 diagnostic plot for sensitivity or power curve
effect_size | Effect-size converters for eliciting assumptions
fit_model | Fit a model for a single simulated dataset
mixpower | Simulation-Based Power Analysis for Mixed-Effects Models
mp_assumptions | Create modeling assumptions for simulation-based power
mp_backend | MixPower backend contract
mp_backend_glmmtmb | Build a glmmTMB backend for Gaussian LMM scenarios
mp_backend_lme4 | Build an lme4 backend for MixPower scenarios
mp_backend_lme4_binomial | Build an lme4 backend for binomial GLMM scenarios
mp_backend_lme4_nb | Build an lme4 backend for Negative Binomial GLMM scenarios
mp_backend_lme4_poisson | Build an lme4 backend for Poisson GLMM scenarios
mp_beta_to_d | Effect-size converters for eliciting assumptions
mp_beta_to_r2 | Effect-size converters for eliciting assumptions
mp_bundle_results | Bundle results with manifest and optional labels
mp_calibrate | Check the Type I error calibration of a scenario's test
mp_compare_models | Compare analysis models on the same simulated data
mp_design | Create a study design specification
mp_d_to_beta | Effect-size converters for eliciting assumptions
mp_extend | Scale a fitted-model scenario's sample size up or down
mp_from_fit | Build a power scenario from a fitted lme4 model
mp_f_to_beta | Effect-size converters for eliciting assumptions
mp_grid_sample_size | Create a grid of values for sample-size search
mp_icc_to_sd | Effect-size converters for eliciting assumptions
mp_logodds_to_or | Effect-size converters for eliciting assumptions
mp_manifest | Reproducibility manifest for power analyses
mp_methods_text | Generate a methods paragraph for a power analysis
mp_missing | Add a missing-data / dropout mechanism to a scenario
mp_or_to_logodds | Effect-size converters for eliciting assumptions
mp_power | Simulation-based power estimation (engine-agnostic core)
mp_power_checkpoint | Resumable, checkpointed power simulation
mp_power_curve | Power curve for a single design/assumption parameter
mp_power_curve_parallel | Parallel power curve evaluation
mp_quick_power | Quick power run for a single LMM design
mp_r2_to_beta | Effect-size converters for eliciting assumptions
mp_recommend_method | Recommend an inference method for a scenario
mp_report_table | Publication-ready summary table for power results
mp_safeguard_effect | Safeguard (confidence-bound) effect size from a fitted model
mp_scenario | Create a power-analysis scenario
mp_scenario_glmmtmb_lmm | Gaussian LMM scenario using glmmTMB
mp_scenario_lme4 | Create a fully specified MixPower scenario with the lme4 backend
mp_scenario_lme4_binomial | Create a fully specified MixPower scenario with the binomial lme4 backend
mp_scenario_lme4_nb | Create a fully specified MixPower scenario with the NB lme4 backend
mp_scenario_lme4_poisson | Create a fully specified MixPower scenario with the Poisson lme4 backend
mp_sd_to_icc | Effect-size converters for eliciting assumptions
mp_sensitivity | Run power sensitivity analysis over a parameter grid
mp_sensitivity_parallel | Parallel sensitivity analysis over a parameter grid
mp_sesoi | Set a smallest effect size of interest (SESOI) on a scenario
mp_solve_sample_size | Solve for minimum sample size achieving target power
mp_t_to_beta | Effect-size converters for eliciting assumptions
mp_write_results | Write results or bundle to CSV or JSON
plot.mp_power | Plot the p-value distribution of a power analysis
plot.mp_power_curve | Plot a power curve
plot.mp_sensitivity | Plot a sensitivity analysis
plot_power | Plot power results
run_parallel | Placeholder for parallel execution
simulate_glmm_binomial_data | Simulate binary outcome data for a GLMM with random effects
simulate_glmm_nb_data | Simulate count outcome data for a Negative Binomial GLMM with random effects
simulate_glmm_poisson_data | Simulate count outcome data for a Poisson GLMM with random effects
simulate_power | Run a simple simulation-based power study
summarize_simulations | Summarize simulation outputs
test_effect | Extract a test statistic for a model term
validate_mp_backend | Validate a MixPower backend