Simulation-Based Power Analysis for Mixed-Effects Models
![[R logo]](../../../doc/html/Rlogo.svg)
![[Up]](../../../doc/html/left.jpg)
![[Top]](../../../doc/html/up.jpg)
Documentation for package ‘mixpower’ version 0.1.0
Help Pages
| fit_model | Fit a model for a single simulated dataset |
| mp_assumptions | Create modeling assumptions for simulation-based power |
| 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_bundle_results | Bundle results with manifest and optional labels |
| mp_design | Create a study design specification |
| mp_manifest | Reproducibility manifest for power analyses |
| mp_power | Simulation-based power estimation (engine-agnostic core) |
| mp_power_curve | Power curve for a single design/assumption parameter |
| mp_power_curve_parallel | Parallel power curve evaluation |
| mp_report_table | Publication-ready summary table for power results |
| mp_scenario | Create a power-analysis scenario |
| 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_sensitivity | Run power sensitivity analysis over a parameter grid |
| mp_solve_sample_size | Solve for minimum sample size achieving target power |
| mp_write_results | Write results or bundle to CSV or JSON |
| plot.mp_power_curve | Plot a power curve |
| plot.mp_sensitivity | Plot a one-dimensional sensitivity curve |
| plot_power | Plot power results |
| run_parallel | Placeholder for parallel execution |
| simulate_glmm_binomial_data | Simulate binary outcome data for a GLMM with random intercepts |
| simulate_glmm_nb_data | Simulate count outcome data for a Negative Binomial GLMM |
| simulate_glmm_poisson_data | Simulate count outcome data for a Poisson GLMM with random intercepts |
| simulate_power | Run a simple simulation-based power study |
| summarize_simulations | Summarize simulation outputs |
| test_effect | Extract a test statistic for a model term |