Simulating bivariate survival data from various copula models. Estimating bivariate copula models with semiparametric or Weibull margins under various copulas. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for the Gumbel and Clayton copulas is also implemented for semiparametric models. See Emura, Lin and Wang (2010) <doi:10.1016/j.csda.2010.03.013> for details.

This package provides methods for computing and visualizing wildfire ignition exposure and directional vulnerability that are published in a series of scientific publications are automated by the functions in this package. See Beverly et al. (2010) <doi:10.1071/WF09071>, Beverly et al. (2021) <doi:10.1007/s10980-020-01173-8>, and Beverly and Forbes (2023) <doi:10.1007/s11069-023-05885-3> for background and methodology.

Converts R objects such as data frames, lists and vectors into JSON strings, and sf spatial objects into GeoJSON'. The core encoders are implemented in Rust using the extendr framework and are designed to efficiently serialize large tabular and spatial datasets. Returns serialized JSON text, allowing applications such as shiny or web APIs to transfer data to client-side JavaScript libraries without additional encoding overhead.

This package provides a collection of functions for computing fairness metrics for machine learning and statistical models, including confidence intervals for each metric. The package supports the evaluation of group-level fairness criterion commonly used in fairness research, particularly in healthcare for binary protected attributes. It is based on the overview of fairness in machine learning written by Gao et al (2025) <doi:10.1002/sim.70234>.

Scrapes and tidies publicly available data from the Women's Tennis Association website (<https://www.wtatennis.com>). Provides helpers to retrieve player biographies, singles and doubles career overviews, match histories, live rankings and aggregate statistics. Dynamic pages are rendered through a headless Chrome session so JavaScript'-generated content is fully captured, and all outputs are returned as tidy data frames suitable for downstream analysis or visualisation.

Reliability and agreement analyses often have limited software support. Therefore, this package was created to make agreement and reliability analyses easier for the average researcher. The functions within this package include simple tests of agreement, agreement analysis for nested and replicate data, and provide robust analyses of reliability. In addition, this package contains a set of functions to help when planning studies looking to assess measurement agreement.

Offers a comprehensive approach for analysing stratified 2x2 contingency tables. It facilitates the calculation of odds ratios, 95% confidence intervals, and conducts chi-squared, Cochran-Mantel-Haenszel, Mantel-Haenszel, and Breslow-Day-Tarone tests. The package is particularly useful in fields like epidemiology and social sciences where stratified analysis is essential. The package also provides interpretative insights into the results, aiding in the understanding of statistical outcomes.

Presents a series of molecular and genetic routines in the R environment with the aim of assisting in analytical pipelines before and after the use of asreml or another library to perform analyses such as Genomic Selection or Genome-Wide Association Analyses. Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) <https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/ASRgenomics_Manual.pdf>.

Downloads wrangled Colombian socioeconomic, geospatial,population and climate data from DANE <https://www.dane.gov.co/> (National Administrative Department of Statistics) and IDEAM (Institute of Hydrology, Meteorology and Environmental Studies). It solves the problem of Colombian data being issued in different web pages and sources by using functions that allow the user to select the desired database and download it without having to do the exhausting acquisition process.

Creation and selection of (Advanced) Coupled Matrix and Tensor Factorization (ACMTF) and ACMTF-Regression (ACMTF-R) models. Selection of the optimal number of components can be done using ACMTF_modelSelection() and ACMTFR_modelSelection()'. The CMTF and ACMTF methods were originally described by Acar et al., 2011 <doi:10.48550/arXiv.1105.3422> and Acar et al., 2014 <doi:10.1186/1471-2105-15-239>, respectively.

This package implements the distribution-free goodness-of-fit regression testing procedure, introduced by Estate Khmaladze (2021, <doi:10.1007/s10463-021-00786-3>) to test whether or not the mean structure of a parametric model belongs to a specified model family. The test is implemented for general mean functions with minimal distributional assumptions as well as common models (e.g., lm, glm) with the usual model assumptions.

This package produces tables for descriptive epidemiological analysis. These tables include attack rates, case fatality ratios, and mortality rates (with appropriate confidence intervals), with additional functionality to calculate Mantel-Haenszel odds, risk, and incidence rate ratios. The methods implemented follow standard epidemiological approaches described in Rothman et al. (2008, ISBN:978-0-19-513554-2). This package is part of the R4EPIs project <https://R4EPI.github.io/sitrep/>.

This package provides a collection of layers for ggplot2'. Provides geoms built on linear and radial gradients from the grid package, giving areas, bars, paths, rectangles, and ridgelines a fading or glowing visual effect. Also includes mathematically driven layers â catenary curves, Chaikin's corner-cutting smoothing (Chaikin, 1974, <doi:10.1016/0146-664X(74)90028-8>), and Fourier-series reconstruction â plus Lexis diagrams, isotype bar charts.

This package provides a consistent API for hypothesis testing built on principles from Structure and Interpretation of Computer Programs': data abstraction, closure (combining tests yields tests), and higher-order functions (transforming tests). Implements z-tests, Wald tests, likelihood ratio tests, Fisher's method for combining p-values, and multiple testing corrections. Designed for use by other packages that want to wrap their hypothesis tests in a consistent interface.

Simulates outcomes of an updated version of the latent state reinforcement learning model originally described in Cochran and Cisler (2019) <doi:10.1371/journal.pcbi.1007331>. The package is designed to create results under all reasonable experiment setups, including different reinforcement schedules, number of cues, number of phases, and number of options per trial. Participants can be simulated using either fixed parameters or parameters drawn from a distribution.

Apply tests of multiple comparisons based on studentized midrange and range distributions. The tests are: Tukey Midrange ('TM test), Student-Newman-Keuls Midrange ('SNKM test), Means Grouping Midrange ('MGM test) and Means Grouping Range ('MGR test). The first two tests were published by Batista and Ferreira (2020) <doi:10.1590/1413-7054202044008020>. The last two were published by Batista and Ferreira (2023) <doi:10.28951/bjb.v41i4.640>.

The primary aim of MasterBayes is to use Markov chain Monte Carlo (MCMC) techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data (Hadfield et al. (2006) <doi:10.1111/j.1365-294X.2006.03050.x>). Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled C++ for efficiency.

Calculate and visualise the financial impact of using a classification model, such as a churn model, to target customers. Provides cost, revenue, profit and return-on-investment curves as a function of the share of customers targeted, cumulative gains and lift, marginal profit per bin, and confusion-matrix based payoff across probability thresholds. Also includes ggplot2 autoplot() methods and an interactive shiny application for exploring the results.

Runtime OpenCL support for R package developers: probe hardware and drivers, load and concatenate kernel sources, and manage dependency-annotated .cl libraries, so packages like nmathopencl and other ported libraries can offer GPU acceleration without each re-implementing and related helpers. Vignettes illustrate integration with suggested package nmathopencl and with downstream applications such as glmbayes'; production kernels for those applications ship in those packages rather than here.

Creativity research involves the need to score open-ended problems. Usually done by humans, automatic scoring using AI becomes more and more accurate. This package provides a simple interface to the Open Scoring API <https://openscoring.du.edu/docs>, leading creativity scoring technology by Organiscak et al. (2023) <doi:10.1016/j.tsc.2023.101356>. With it, you can score your own data directly from an R script.

This package provides seamless access to the QGIS (<https://qgis.org>) processing toolbox using the standalone qgis_process command-line utility. Both native and third-party (plugin) processing providers are supported. Beside referring data sources from file, also common objects from sf', terra and stars are supported. The native processing algorithms are documented by QGIS.org (2024) <https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/>.

This package provides methods for managing under- and over-enrollment in Simon's Two-Stage Design are offered by providing adaptive threshold adjustments and sample size recalibration. It also includes post-inference analysis tools to support clinical trial design and evaluation. The package is designed to enhance flexibility and accuracy in trial design, ensuring better outcomes in oncology and other clinical studies. Yunhe Liu, Haitao Pan (2024). Submitted.

This package provides tools for working with Type S (Sign) and Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000) <doi:10.1007/s001800000040> and Gelman & Carlin (2014) <doi:10.1177/1745691614551642>. In addition to simply calculating the probability of Type S/M error, the package includes functions for calculating these errors across a variety of effect sizes for comparison, and recommended sample size given "tolerances" for Type S/M errors. To improve the speed of these calculations, closed forms solutions for the probability of a Type S/M error from Lu, Qiu, and Deng (2018) <doi:10.1111/bmsp.12132> are implemented. As of 1.0.0, this includes support only for simple research designs. See the package vignette for a fuller exposition on how Type S/M errors arise in research, and how to analyze them using the type of design analysis proposed in the above papers.

The cfToolsData package supplies the data for the cfTools package. It contains two pre-trained deep neural network (DNN) models for the cfSort function. Additionally, it includes the shape parameters of beta distribution characterizing methylation markers associated with four tumor types for the CancerDetector function, as well as the parameters characterizing methylation markers specific to 29 primary human tissue types for the cfDeconvolve function.