Introduction | Exponential Distribution | Weibull distribution | Gompertz distribution | Piecewise Exponential distribution | Log-logistic distribution | Log-Normal distribution | Comparison of SURVIVAL objects | References

Introduction | Generation of Proportional Hazard times | Generation of Acceleration Failure Times | Generation of Extended Hazards times | Simulating recurrent episodes | Renewal process | Non-homogeneous Poisson process | References

Introduction | Trial simulation | Analysis of the simulation | For recurrent events | For first or only event | References

Introduction | Empirical power for superiority | Simulation | Analyze the simulation | Conclusion

Introduction | Empirical power to evaluate non-proportionality of the hazard | Simulation | Analyze the simulation | Conclusion | References

Introduction | Example

Introduction | Example: Equivalence Among Vaccine Lots

Introduction | power_single_rate() | Example 1: 100 Subjects, Power 0.95 | Example 2: 30 Subjects, Power 0.95, 0.90, and 0.8 | Example 3: 500 and 1500 Subjects | power_events_rate() | Example 4: 500 and 1500 Subjects Across Standard Frequency Categories | References

Introduction | Examples with a single outcome | Examples using multiple outcomes | References

Introduction | Examples with a single outcome | Examples using multiple outcomes | References

Introduction | Example

Introduction | Example using synthetic data | The latent class model | Estimating the bayesian latent class model | Bibliography

Introduction | Example using synthetic data | The logit exponential model | Estimating the sensitivity and specificity | Graph of diagnostic characteristics for all cutoff points | ROC curve | Bibliography

Introduction | Setting up targets with repana | Managing Project Artifacts | Running the Analysis | Conclusion

Introduction | Default structure | Section dirs: | Section clean_before_new_analysis: | Section defaultdb: | Section template: | Workflow

Practical example | Description of the DISTRIBUTION object | Factory of DISTRIBUTION objects | Methods | Convolution for Distribution with the same dimensions | Mixture | Convolution of distributions with different dimensions

Introduction | Sample size for a proportion | A new DISTRIBUTION factory | Using the Sample Size object | Enhance the Sample Size object