This package provides a pipeable, transparent implementation of areal weighted interpolation with support for interpolating multiple variables in a single function call. These tools provide a full-featured workflow for validation and estimation that fits into both modern data management (e.g. tidyverse) and spatial data (e.g. sf) frameworks.

Calculate the area of triangles and polygons using the shoelace formula. Area may be signed, taking into account path orientation, or unsigned, ignoring path orientation. The shoelace formula is described at <https://en.wikipedia.org/wiki/Shoelace_formula>.

Generation of natural looking noise has many application within simulation, procedural generation, and art, to name a few. The ambient package provides an interface to the FastNoise C++ library and allows for efficient generation of perlin, simplex, worley, cubic, value, and white noise with optional perturbation in either 2, 3, or 4 (in case of simplex and white noise) dimensions.

Geographic, use, and property related data on airports.

Programming neuroscience specific Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>). This package extends the admiral package.

Construct language-aware lists. Make "and"-separated and "or"-separated lists that automatically conform to the user's language settings.

Adaptive Sparse Multi-block Partial Least Square, a supervised algorithm, is an extension of the Sparse Multi-block Partial Least Square, which allows different quantiles to be used in different blocks of different partial least square components to decide the proportion of features to be retained. The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome.

This package provides clean, tidy access to the Anthropic Economic Index (AEI) dataset hosted on Hugging Face <https://huggingface.co/datasets/Anthropic/EconomicIndex>. The AEI is a recurring release from Anthropic that maps usage of the Claude family of large language models to occupations and tasks using the O*NET taxonomy and the Standard Occupational Classification system, following the methodology of Handa et al. (2025) <doi:10.48550/arXiv.2503.04761> and the privacy-preserving system Clio of Tamkin et al. (2024) <doi:10.48550/arXiv.2412.13678>. Functions list available releases, fetch raw and enriched usage tables, retrieve task statements, request hierarchies, country-level breakdowns, and the standalone labor market impacts tables (job exposure and task penetration), compare two releases, join the index to user-supplied data on a shared key, and compute usage-concentration metrics (Herfindahl-Hirschman Index, top-N concentration ratios, Shannon entropy). Data is cached locally for subsequent calls. Reproducibility helpers produce BibTeX or plain-text citations that include the methodological source paper. This product uses the Anthropic Economic Index data (released under CC-BY by Anthropic') but is not endorsed or certified by Anthropic'.

Automated psychological scale development and structural validation using large language models (LLMs) and network psychometric methods. Implements the AI-GENIE framework (Automatic Item Generation and Validation via Network-Integrated Evaluation) to generate candidate items, compute embedding representations, and estimate dimensional structure using Exploratory Graph Analysis (EGA). Item quality is evaluated using Unique Variable Analysis to identify redundant items and Bootstrap EGA to assess item and dimension stability. Supports both fully automated item generation and analysis of user-provided item sets, facilitating efficient, theory-informed measurement development prior to empirical data collection.

Offers a rich and diverse collection of datasets focused on agriculture, agronomy, animal science, and related fields. The package includes experimental, observational, and field-trial data on crops such as rice, wheat, corn, soybean, cotton, coffee, avocado, and orange, as well as forestry species including bamboo, eucalyptus, and timber. Datasets cover plant breeding and genetics, factorial and randomized block experiments, herbicide and insecticide efficacy trials, pest and disease infestation, soil characteristics and land suitability, plant growth regulators, seed germination, and crop yield modeling. Additional datasets address animal science topics such as cattle insemination and conception, pig and broiler growth, lamb births, and toxicology studies on aquatic and non-target species. Data sources include peer-reviewed agronomic studies, uniformity and Latin square field trials, glasshouse experiments, and international agricultural surveys. Designed for agronomists, researchers, plant and animal scientists, data scientists, and students, this package facilitates exploratory data analysis, statistical modeling, and hypothesis testing in agricultural and biological sciences. The package includes datasets originally distributed in other R packages. The original authors and contributors associated with these source packages and datasets are acknowledged in Authors@R, and the original sources and applicable licensing terms are documented in LICENSES_DETAILS.md.

Developed as an R alternative to the AeroEvap model developed by the Desert Research Institute (DRI) in python <https://github.com/WSWUP/AeroEvap/blob/master/README.rst> which estimates open water evaporation using the aerodynamic mass transfer approach.

This package provides novel nonparametric tests, APCSSA and APCSSM', for interaction in two-way ANOVA designs with balanced replications using all possible comparisons. These statistics extend previous methods, allow greater flexibility, and demonstrate higher power in detecting interactions for non-normal data. The package includes optimized functions for computing these test statistics, generating interaction plots, and simulating their null distributions. The companion package APCinteractionData is available on GitHub <https://github.com/tranbaokhue/APCinteractionData>. Methods are described and compared empirically in Tran, Wagaman, Nguyen, Jacobson, and Hartlaub (2024) <doi:10.48550/arXiv.2410.04700>.

Converts legacy microscopy video formats (H.264/H.265, AVI/MJPEG, TIFF stacks) to the modern AV1 codec with minimal quality loss. Typical use cases include compressing large TIFF stacks from confocal microscopy and time-lapse experiments from hundreds of gigabytes to manageable sizes, re-encoding MP4 files exported from CellProfiler', ImageJ'/'Fiji', and microscope software with approximately 2x better compression at the same visual quality, and converting legacy AVI (MJPEG) and H.265 recordings to a single patent-free format suited for long-term archival. Automatically selects the best available backend: GPU hardware acceleration via Vulkan VK_KHR_VIDEO_ENCODE_AV1 or VAAPI (tested on AMD RDNA4; bundled headers, builds with any Vulkan SDK >= 1.3.275), with automatic fallback to CPU encoding through FFmpeg and SVT-AV1'. User controls quality via a single CRF parameter; each backend adapts automatically (CPU and Vulkan use CRF directly, VAAPI targets 55 percent of input bitrate). TIFF stacks use near-lossless CRF 5 by default, with optional proportional scaling via tiff_scale (multiplier or bounding box, aspect ratio always preserved). Small frames are automatically scaled up to meet hardware encoder minimums. Audio tracks are preserved automatically. Provides a simple R API for batch conversion of entire experiment folders.

This package provides tools to study sorting patterns in matching markets and to estimate the affinity matrix of both the bipartite one-to-one matching model without frictions and with Transferable Utility by Dupuy and Galichon (2014) <doi:10.1086/677191> and its unipartite variant by Ciscato', Galichon and Gousse (2020) <doi:10.1086/704611>. It also contains all the necessary tools to implement the saliency analysis, to run rank tests of the affinity matrix and to build tables and plots summarizing the findings.

Simulating and estimating asymmetric peer-effect models (Houndetoungan and Lambotte, 2026 <doi:10.48550/arXiv.2608.09219>). The model nests the widely used linear-in-means model (Manski, 1993 <doi:10.2307/2298123>; Bramoulle et al., 2009 <doi:10.1016/j.jeconom.2008.12.021>) and allows agents to be influenced differently by friends who exert more or less effort than themselves.

Estimate the causal treatment effect for subjects that can adhere to one or both of the treatments. Given longitudinal data with missing observations, consistent causal effects are calculated. Unobserved potential outcomes are estimated through direct integration as described in: Qu et al., (2019) <doi:10.1080/19466315.2019.1700157> and Zhang et. al., (2021) <doi:10.1080/19466315.2021.1891965>.

This wrapper package for mgcv makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, AutoGAM tries to automate the procedure of configuring a highly accurate GAM which performs at reasonably high speed, even for large datasets.

This package provides a collection of efficient functions for working with individual ages and corresponding intervals. These include functions for conversion from an age to an interval, aggregation of ages with associated counts in to intervals and the splitting of interval counts based on specified age distributions.

Estimate diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where can not measure the golden standard but can estimate it using the attributable fraction.

The functions proposed in this package allows to evaluate the process of measurement of the chemical components of water numerically or graphically. TSSS(), ICHS and datacheck() functions are useful to control the quality of measurements of chemical components of a sample of water. If one or more measurements include an error, the generated graph will indicate it with a position of the point that represents the sample outside the confidence interval. The function CI() allows to evaluate the possibility of contamination of a water sample after being obtained. Validation() is a function that allows to calculate the quality parameters of a technique for the measurement of a chemical component.

Computation of the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object.

This is an implementation of the Generalized Discrimination Score (also known as Two Alternatives Forced Choice Score, 2AFC) for various representations of forecasts and verifying observations. The Generalized Discrimination Score is a generic forecast verification framework which can be applied to any of the following verification contexts: dichotomous, polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble. A comprehensive description of the Generalized Discrimination Score, including all equations used in this package, is provided by Mason and Weigel (2009).

This package provides capabilities to process Apache HTTPD Log files.The main functionalities are to extract data from access and error log files to data frames.

This package provides a novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper<doi:10.2196/21798>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.