Implements HSROC (hierarchical summary receiver operating characteristic) model developed by Ma, Lian, Chu, Ibrahim, and Chen (2018) &lt;<a href="https://doi.org/10.1093%2Fbiostatistics%2Fkxx025" target="_top">doi:10.1093/biostatistics/kxx025</a>&gt; and hierarchical model developed by Lian, Hodges, and Chu (2019) &lt;<a href="https://doi.org/10.1080%2F01621459.2018.1476239" target="_top">doi:10.1080/01621459.2018.1476239</a>&gt; for performing meta-analysis for 1-5 diagnostic tests to simultaneously compare multiple tests within a missing data framework. This package evaluates the accuracy of multiple diagnostic tests and also gives graphical representation of the results.

Tools for competing risks trials that allow simultaneous inference on recovery and mortality endpoints. Provides data preparation helpers, standard cumulative incidence estimators (restricted mean time gained/lost), and severity weighted extensions that integrate longitudinal ordinal outcomes to summarise treatment benefit. Methods follow Wen, Wang, and Hu (2023) Biometrics 79(3):1635-1645 &lt;<a href="https://doi.org/10.1111%2Fbiom.13752" target="_top">doi:10.1111/biom.13752</a>&gt;.

Implements GINA-X, a genome-wide iterative fine-mapping method designed for non-Gaussian traits. It supports the identification of credible sets of genetic variants.

This contains functions that can be used to estimate a smoothed and a non-smoothed (empirical) time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve for correlated right-censored time-to-event data. See Beyene and Chen (2024) &lt;<a href="https://doi.org/10.1177%2F09622802231220496" target="_top">doi:10.1177/09622802231220496</a>&gt;.

Implements the Fixed Effect Jackknife Instrumental Variables ('FEJIV') estimator of Chao, Swanson, and Woutersen (2023) &lt;<a href="https://doi.org/10.1016%2Fj.jeconom.2022.12.011" target="_top">doi:10.1016/j.jeconom.2022.12.011</a>&gt;, allowing consistent IV estimation with many (possibly weak) instruments, cluster fixed effects, heteroskedastic errors, and many exogenous covariates. The estimator is recommended by Słoczyński (2024) &lt;<a href="https://doi.org/10.48550%2FarXiv.2011.06695" target="_top">doi:10.48550/arXiv.2011.06695</a>&gt; as an alternative to two-stage least squares when estimating the interacted specification of Angrist and Imbens (1995) &lt;<a href="https://doi.org/10.1080%2F01621459.1995.10476535" target="_top">doi:10.1080/01621459.1995.10476535</a>&gt;.

https://cran.r-project.org/package=mumarinex

Comprehensively identifying states and state-like actors is difficult. This package provides data on states and state-like entities in the international system across time. The package combines and cross-references several existing datasets consistent with the aims and functions of the manydata package. It also includes functions for identifying state references in text, and for generating fictional state names.

Provides functions for planning clinical trials subject to a delayed treatment effect using assurance-based methods. Includes two 'shiny' applications for interactive exploration, simulation, and visualisation of trial designs and outcomes. The methodology is described in: Salsbury JA, Oakley JE, Julious SA, Hampson LV (2024) "Assurance methods for designing a clinical trial with a delayed treatment effect" &lt;<a href="https://doi.org/10.1002%2Fsim.10136" target="_top">doi:10.1002/sim.10136</a>&gt;, Salsbury JA, Oakley JE, Julious SA, Hampson LV (2024) "Adaptive clinical trial design with delayed treatment effects using elicited prior distributions" &lt;<a href="https://doi.org/10.48550%2FarXiv.2509.07602" target="_top">doi:10.48550/arXiv.2509.07602</a>&gt;.

https://cran.r-project.org/package=carbonpredict