Provides a broad-view perspective on data via linear mapping of data onto a radial coordinate system. The package contains functions to visualize the residual values of linear regression and Cartesian data in the defined radial scheme. See the 'pacviz' documentation page for more information: &lt;<a href="https://pacviz.sriley.dev/" target="_top">https://pacviz.sriley.dev/</a>&gt;.

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.

Computation of the multivariate marine recovery index, including functions for data visualization and ecological diagnostics of marine ecosystems. The computational details are described in the original publication. Reference: Chauvel, N., Grall, J., Thiébaut, E., Houbin, C., Pezy, J.P. (in press). "A general-purpose Multivariate Marine Recovery Index for quantifying the influence of human activities on benthic habitat ecological status". Ecological Indicators.

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.

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;.

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;.

Predict Scope 1, 2 and 3 carbon emissions for UK Small and Medium-sized Enterprises (SMEs), using Standard Industrial Classification (SIC) codes and annual turnover data. The 'carbonpredict' package provides single and batch prediction, plotting, and workflow tools for carbon accounting and reporting. The package utilises pre-trained models, leveraging rich classified transaction data to accurately predict Scope 1, 2 and 3 carbon emissions for UK SMEs as well as identifying emissions hotspots. The methodology used to produce the estimates in this package is fully detailed in the following peer-reviewed publication in the Journal of Industrial Ecology: Phillpotts, A., Owen. A., Norman, J., Trendl, A., Gathergood, J., Jobst, Norbert., Leake, D. (2025) &lt;<a href="https://doi.org/10.1111%2Fjiec.70106" target="_top">doi:10.1111/jiec.70106</a>&gt; "Bridging the SME Reporting Gap: A New Model for Predicting Scope 1 and 2 Emissions".

Data files and a few functions used in the book 'Linear Models and Regression with R: An Integrated Approach' by Debasis Sengupta and Sreenivas Rao Jammalamadaka (2019).