The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance.

This package provides a complete data set of historic GB trig points in British National Grid (OSGB36) coordinate reference system. Trig points (aka triangulation stations) are fixed survey points used to improve the accuracy of map making in Great Britain during the 20th Century. Trig points are typically located on hilltops so still serve as a useful navigational aid for walkers and hikers today.

Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].

This package implements the tail-rank statistic for selecting biomarkers from a microarray data set, an efficient nonparametric test focused on the distributional tails. See <https://gitlab.com/krcoombes/coombeslab/-/blob/master/doc/papers/tolstoy-new.pdf>.

Transformer is a Deep Neural Network Architecture based i.a. on the Attention mechanism (Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>).

Comprehensive functions to calculate sample size and power for clinical trials with two co-primary endpoints. The package supports five endpoint combinations: two continuous endpoints (Sozu et al. 2011 <doi:10.1080/10543406.2011.551329>), two binary endpoints using asymptotic methods (Sozu et al. 2010 <doi:10.1002/sim.3972>) and exact methods (Homma and Yoshida 2025 <doi:10.1177/09622802251368697>), mixed continuous and binary endpoints (Sozu et al. 2012 <doi:10.1002/bimj.201100221>), and mixed count and continuous endpoints (Homma and Yoshida 2024 <doi:10.1002/pst.2337>). All methods appropriately account for correlation between endpoints and provide both sample size and power calculation capabilities.

User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) <DOI:10.3758/s13428-017-0869-7> and a tutorial on MPT modeling can be found in Schmidt, Erdfelder, & Heck (2023) <DOI:10.1037/met0000561>.

An object model for source text and translations. Find and extract translatable strings. Provide translations and seamlessly retrieve them at runtime.

Built on top of the tibble package, tibbletime is an extension that allows for the creation of time aware tibbles. Some immediate advantages of this include: the ability to perform time-based subsetting on tibbles, quickly summarising and aggregating results by time periods, and creating columns that can be used as dplyr time-based groups.

Visualizing cuts for either axis-align or non axis-align tree methods (e.g. decision tree, random tessellation process).

This package provides two classes extending data.table class. Simple tableList class wraps data.table and any additional structures together. More complex tableMatrix class combines data.table and matrix'. See <http://github.com/InferenceTechnologies/tableMatrix> for more information and examples.

Interacts with a suite of web application programming interfaces (API) for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more. Some of the services supported include NCBI E-utilities (<https://www.ncbi.nlm.nih.gov/books/NBK25501/>), Encyclopedia of Life (<https://eol.org/docs/what-is-eol/data-services>), Global Biodiversity Information Facility (<https://techdocs.gbif.org/en/openapi/>), and many more. Links to the API documentation for other supported services are available in the documentation for their respective functions in this package.

Split a dataframe, tibble, or data.table into training and test sets. Return either a list, an index, or directly assign training and test sets into memory.

Estimation of time of concentration and lag times for watersheds based on their morphometric characteristics. It includes various methods for calculation and offers plotting functionalities for comparative analysis. For more details see Bransby-Williams (1922, ISSN 2214-5818), Kirpich (1940) <https://hess.copernicus.org/articles/24/2655/2020/>, Kerby (1959, ISBN-13, 979-8355357214), Johnstone & Cross (1949, ISBN:9780823211234), California Division of Highways (1942, ISSN:0012-7353), Clark (1945) <doi:10.1061/TACEAT.0005800>, Giandotti (1934) <doi:10.1080/02626667.2017.1384549>, Passini (1972, ISBN:84-7433-040-8), Témez (1978, ISBN:84-7433-040-8), Pérez (1962, ISSN:0012-7353), Pilgrim (1977) <doi:10.1029/WR013i003p00587>, Bureau of Reclamation (1973, ISBN:9780913232123), Valencia-Zuluaga (1983) <https://repositorio.unal.edu.co/>, Ventura & Heras (1964) <doi:10.1061/9780784413548.005>, Soil Conservation Service (1972, ISBN:OL15009517M), Soil Conservation Service (1986) <https://www.ars.usda.gov/research/software/download/?softwareid=527>, US Navy - Technical Publication Navdocks (1972) <ISBN:978-1289256234>, Federal Aviation Administration (1970, ISBN:9780913236543), Natural Environment Research Council (1975, ISBN:9780114501234), Mimikou (1984) <doi:10.1080/02626668409490922>, Watt & Chow (1985) <doi:10.1139/l85-031>, Haktanir & Sezen (1990) <doi:10.1080/02626669009492423>.

Generate LaTeX tables directly from R. It builds LaTeX tables in blocks in the spirit of ggplot2 using the + and / operators for concatenation in the vertical and horizontal dimensions, respectively. It exports tables in the LaTeX tabular environment using .tex code. It can compile .tex code to PDF automatically.

Handling and manipulation polygons, coordinates, and other geographical objects. The tools include: polygon areas, barycentric and trilinear coordinates (Hormann and Floater, 2006, <doi:10.1145/1183287.1183295>), convex hull for polygons (Graham and Yao, 1983, <doi:10.1016/0196-6774(83)90013-5>), polygon triangulation (Toussaint, 1991, <doi:10.1007/BF01905693>), great circle and geodesic distances, Hausdorff distance, and reduced major axis.

Download taxonomic databases, convert them into SQLite format, and query them locally for fast, reliable, and reproducible access to taxonomic data.

Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for Latent Dirichlet Allocation and Correlated Topic Models fit using the topicmodels package. For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) <arXiv:1206.4631v1>.

This package provides methods for handling the missing values outliers are introduced in this package. The recognized missing values and outliers are replaced using a model-based approach. The model may consist of both autoregressive components and external regressors. The methods work robust and efficient, and they are fully tunable. The primary motivation for writing the package was preprocessing of the energy systems data, e.g. power plant production time series, but the package could be used with any time series data. For details, see Narajewski et al. (2021) <doi:10.1016/j.softx.2021.100809>.

Propensity score matching for non-binary treatments.

Generate a palette of tints, shades or both from a single colour.

This package provides functions for statistical analysis, prediction and control of time series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.

This package provides a collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes. These tools are derived from Anderson et al. (2001) <doi:10.1016/S0047-2727(00)00085-2> and Froeb et al. (2003) <doi:10.1016/S0304-4076(02)00166-5>.

This package implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) <doi:10.48550/arXiv.2506.19988>. Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses.