Package: TOSTER 0.8.6

Introduction to TOST | Beyond Equivalence: Minimal Effects Testing | Getting Started with Sample Data | Independent Groups Analysis | Basic Equivalence Testing | Using t_TOST | Using simple_htest | Viewing Results | Visualizing Results | 1. Simple Dot-and-Whisker Plot | 2. Consonance Density Plot | 3. Consonance Plot | 4. Null Distribution Plot | Describing Results in Plain Language | Paired Samples Analysis | Using the Sleep Data with Paired Analysis | Using Separate Vectors for Paired Data | Minimal Effect Testing (MET) | Interpreting MET Results | Warning: careful using narrow equivalence bounds | One Sample t-test | Working with Summary Statistics Only | Power Analysis for TOST | Conclusion | References

Simple Correlation Test | Advantages of z_cor_test | Using Summary Statistics | Bootstrapped Correlation Test | Robust Correlation Methods | Comparing Correlations | Summary Statistics Approach | Bootstrapped Comparison | Practical Recommendations | Advanced Usage | Custom Bootstrap Methods | Working with Missing Data | References

Introduction | The Theory Behind F-test Equivalence Testing | Practical Application with TOSTER | Setting Up | Traditional ANOVA | Equivalence Testing Using the Raw F-statistic | Interpreting the Results | Equivalence Testing Using ANOVA Objects | Minimal Effect Testing | Visualizing Partial Eta-Squared | A Second Example: Small Effect | Power Analysis for F-test Equivalence Testing | Sample Size Calculation | Power Calculation | Detectable Equivalence Bound | Choosing Appropriate Equivalence Bounds | Conclusion | References

Introduction to Robust TOST Methods | When to Use Robust TOST Methods | Wilcoxon TOST | Key Features and Usage | Interpreting the Results | Rank-Biserial Correlation | Confidence Intervals | Conversion to other effect sizes | Guidelines for Selecting Effect Size Measures | Brunner-Munzel | Overview and Advantages | Basics of the Calculative Approach | Example | Interpreting Brunner-Munzel Results | When to Use Permutation | Bootstrap TOST | Advantages of Bootstrapping | Two Sample Algorithm | Choosing the Number of Bootstrap Replications | Interpreting Bootstrap TOST Results | Ratio of Difference (Log Transformed) | Why Use The Natural Log Transformation? | Applications Beyond Bioequivalence | log_TOST | Interpreting the Means Ratio | Bootstrap + Log | Just Estimate an Effect Size | Choosing Between Different Bootstrap CI Methods | Summary Comparison of Robust TOST Methods | Conclusion | References

Bias Correction (Hedges) | Independent Samples | Variances Assumed Unequal: Cohen's d(av) | Variances Assumed Equal: Cohen's d | Glass's Delta | Paired Samples | Cohen's d(z): Change Scores | Cohen's d(rm): Correlation Corrected | One Sample | Confidence Intervals | @Cousineau2021 Method (smd_ci = "goulet") | The non-central t-method (smd_ci = "nct") | Central t-method | Normal method | Just Estimate the SMD | Plotting SMDs | Comparing SMDs | Raw Data | Visualize | References

Equivalence Testing with TOSTER | Equivalence Test for Meta-analysis | Independent Groups Equivalence Test | Independent Groups Student's Equivalence Test | Independent Groups Welch's Equivalence Test | One-Sample Equivalence Test | Equivalence test for correlations