Provides functions to estimate the proportion of treatment effect explained by the surrogate marker using a Bayesian Model Averaging approach. Duan and Parast (2023) <doi:10.1002/sim.9986>.

Estimates the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, et al (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1> and Wang et al (2025) "Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival" <doi:10.1002/sim.70268>.  A tutorial for this package can be found at <https://www.laylaparast.com/surrogateoutcome>.

Provides functions to nonparametrically assess assumptions sufficient to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of conditional mean functions, and non-negative residual treatment effect. Details are described in: Hsiao E, Tian L, and Parast L (2026). "Avoiding the surrogate paradox: an empirical framework for assessing assumptions." Journal of Nonparametric Statistics <doi:10.1080/10485252.2025.2498609>. There are also functions to assess resilience to the surrogate paradox via calculation of the resilience probability, the resilience bound, and the resilience set. Details will be available in Hsiao E, Tian L, and Parast L, "Resilience Measures for the Surrogate Paradox" (Under Review). Lastly, there is a function to assess resilience to the surrogate paradox in the met-analytic setting, described in Hsiao E and Parast L, "A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience" (Under Review).  A tutorial for this package can be found at <https://www.laylaparast.com/surrogateparadoxtest>.

Uses a novel rank-based nonparametric approach to evaluate a surrogate marker
  in a small sample size setting. Details are described in Parast et al (2024)
  <doi:10.1093/biomtc/ujad035>, in Hughes A et al (2025)
  <doi:10.1002/sim.70241>, and in Hughes A et al (2026) <doi:10.48550/arXiv.2605.03819>. A tutorial for this package can be found at
  <https://www.laylaparast.com/surrogaterank> and a Shiny App 
  implementing the package can be found at 
  <https://parastlab.shinyapps.io/SurrogateRankApp/>.