How valeriepan/survive-380-anna-the-best works
This is an R package and Shiny application for Monte Carlo study of one-sample binomial-proportion z-tests. The documented workflow lets a user vary simulation and test settings, simulate binomial outcomes and p-values, estimate Type I error, Type II error, and power, then inspect a summary table and p-value histogram. The README and file tree support these product capabilities and module paths; source excerpts were unavailable, so the graph avoids asserting specific calls between app modules and package functions.
Main entry points: Function_Code.R Simulation functions · server-plots.R Plot handling · app.R Shiny app · ui-sim_input.R Simulation controls
Components 12
Interactive app
- Shiny app
shiny-app/app.R - Simulation controls
shiny-app/R/ui-sim_input.R - Plot handling
shiny-app/server-plots.R
Simulation and tests
- Simulation functions
R/Function_Code.R - Binomial p-values
R/Function_Code.R - Null p-values
R/Function_Code.R
Results and visualization
- Type I error
R/Function_Code.R - Type II and power
R/Function_Code.R - Power curve
R/Function_Code.R - Error and power table
- P-value histogram
Other components
- User
Connections 13
- User → Shiny app: interacts with
- User → Simulation controls: adjusts
- Simulation functions → Binomial p-values: computes
- Simulation functions → Null p-values: computes
- Simulation functions → Type I error: estimates
- Simulation functions → Type II and power: estimates
- Simulation functions → Power curve: estimates
- Type I error → Error and power table: reports
- Type II and power → Error and power table: reports
- Power curve → Error and power table: reports
- Binomial p-values → P-value histogram: provides p-values
- Null p-values → P-value histogram: provides p-values
- Plot handling → P-value histogram: renders