Package index • discourse

All functions

check_grim(): Check plausibility of a reported mean with the GRIM test (Brown & Heathers 2007)

discourse discourse-package: discourse: Data-simulation via Iterative Stochastic Combinatorial Optimization Using Reported Summary Estimates

get_rmse(): Compute RMSE for a single discourse.object result

get_rmse_parallel(): Compute RMSE metrics across discourse.object runs

get_stats(): Extract statistics from a single discourse.object

get_stats_parallel(): Aggregate statistics across discourse.object runs

hill_climb(): Perform hill-climbing optimization

long_to_wide(): Reshape Data from Long to Wide Format

optim_aov(): Optimize simulated data to match ANOVA F-values

optim_lm(): Optimize simulated data to match target correlations and fixed-effects regression estimates

optim_lme(): Optimize simulated longitudinal mixed-effects data to match target correlations and regression estimates

optim_vec(): Optimize a vector or matrix to match target means and SDs

parallel_aov(): #' Optimize multiple data sets to match ANOVA F-values

parallel_lm(): Optimize multiple simulated data sets to match target correlations and fixed-effects regression fits

parallel_lme(): Optimize multiple simulated longitudinal mixed-effects data to match target correlations and regression estimates

plot_cooling(): Plot cooling schedule of a discourse.object

plot_error(): Plot Error Ratio Evolution for a discourse.object

plot_error_ratio(): Plot Error Ratio Evolution for a discourse.object

plot_histogram(): Plot Histograms for Each Variable

plot_partial_regression(): Partial Regression Plots for a Linear Model

plot_rmse(): Plot RMSE Comparison for discourse.object runs

plot_summary(): Plot Summary of Simulated vs. Target Statistics for a discourse.object

print(<summary.discourse.object>): Print summary for summary.discourse.object

run_app(): Run the Shiny Application

summary(<discourse.object>): Summarize a discourse.object

weights_est(): Estimate objective function weights via pilot simulations

weights_vec(): Estimate weighting factors for mean and SD errors

wide_to_long(): Reshape Data from Wide to Long Format