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  "Title": "Empirical Testing of Surrogate Paradox Assumptions",
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  "Authors@R": "c(person(given = \"Emily\", family = \"Hsiao\", role = c(\"aut\")), person(\"Layla\", \"Parast\", email = \"parast@austin.utexas.edu\", role = c(\"aut\", \"cre\")))",
  "Description": "Provides functions to nonparametrically assess assumptions\nsufficient to prevent the surrogate paradox through hypothesis\ntests of stochastic dominance, monotonicity of conditional mean\nfunctions, and non-negative residual treatment effect. Details\nare described in: Hsiao E, Tian L, and Parast L (2026).\n\"Avoiding the surrogate paradox: an empirical framework for\nassessing assumptions.\" Journal of Nonparametric Statistics\n<doi:10.1080/10485252.2025.2498609>. There are also functions\nto assess resilience to the surrogate paradox via calculation\nof the resilience probability, the resilience bound, and the\nresilience set. Details will be available in Hsiao E, Tian L,\nand Parast L, \"Resilience Measures for the Surrogate Paradox\"\n(Under Review). Lastly, there is a function to assess\nresilience to the surrogate paradox in the met-analytic\nsetting, described in Hsiao E and Parast L, \"A Functional-Class\nMeta-Analytic Framework for Quantifying Surrogate Resilience\"\n(Under Review).  A tutorial for this package can be found at\n<https://www.laylaparast.com/surrogateparadoxtest>.",
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