The public available Danish SKS (Sundhedsvæsenets Klassifikationssystem) and SNOMED (Systematized Nomenclature of Medicine) codes with labels. The labels are useful to sort and make sense of microdata in closed computing environments. Current as of april 2026 (SNOMED) and june 2026 (SKS). SNOMED <https://www.patobank.dk/snomed/> and SKS <https://filer.sundhedsdata.dk/sks/data/skscomplete/>.

This package provides tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package secr'.

Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup.

This package provides inference based on the survey package for the wide range of parametric models in the VGAM package.

Spectral and Average Autocorrelation Zero Distance Density ('sazed') is a method for estimating the season length of a seasonal time series. sazed is aimed at practitioners, as it employs only domain-agnostic preprocessing and does not depend on parameter tuning or empirical constants. The computation of sazed relies on the efficient autocorrelation computation methods suggested by Thibauld Nion (2012, URL: <https://etudes.tibonihoo.net/literate_musing/autocorrelations.html>) and by Bob Carpenter (2012, URL: <https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).

Time series area-level models for small area estimation. The package supplements the functionality of the sae package. Specifically, it includes EBLUP fitting of the Rao-Yu model in the original form without a spatial component. The package also offers a modified ("dynamic") version of the Rao-Yu model, replacing the assumption of stationarity. Both univariate and multivariate applications are supported. Of particular note is the allowance for covariance of the area-level sample estimates over time, as encountered in rotating panel designs such as the U.S. National Crime Victimization Survey or present in a time-series of 5-year estimates from the American Community Survey. Key references to the methods include J.N.K. Rao and I. Molina (2015, ISBN:9781118735787), J.N.K. Rao and M. Yu (1994) <doi:10.2307/3315407>, and R.E. Fay and R.A. Herriot (1979) <doi:10.1080/01621459.1979.10482505>.

Analyzing soil food webs or any food web measured at equilibrium. The package calculates carbon and nitrogen fluxes and stability properties using methods described by Hunt et al. (1987) <doi:10.1007/BF00260580>, de Ruiter et al. (1995) <doi:10.1126/science.269.5228.1257>, Holtkamp et al. (2011) <doi:10.1016/j.soilbio.2010.10.004>, and Buchkowski and Lindo (2021) <doi:10.1111/1365-2435.13706>. The package can also manipulate the structure of the food web as well as simulate food webs away from equilibrium and run decomposition experiments.

This package provides interactive Shiny applications for building intuition in statistics and data science. The apps run locally and use visualization, simulation, and repeated sampling to illustrate topics such as probability distributions, permutation tests, analysis of variance, linear regression, and sums of squares.

This package provides a client for running SPARQL queries directly from R. SPARQL (short for SPARQL Protocol and RDF Query Language) is a query language used to retrieve and manipulate data stored in RDF (Resource Description Framework) format.

This package provides tools for using the StreamCat and LakeCat API and interacting with the StreamCat and LakeCat database. Convenience functions in the package wrap the API for StreamCat on <https://api.epa.gov/StreamCat/streams/metrics>.

This package provides a collection of recycled and modified R functions to aid in file manipulation, data exploration, wrangling, optimization, and object manipulation. Other functions aid in convenient data visualization, loop progression, software packaging, and installation.

Manage package documentation and namespaces from the command line. Programmatically attach namespaces in R and Rmd script, populates Roxygen2 skeletons with information scraped from within functions and populate the Imports field of the DESCRIPTION file.

Various tools for semantic vector spaces, such as correspondence analysis (simple, multiple and discriminant), latent semantic analysis, probabilistic latent semantic analysis, non-negative matrix factorization, latent class analysis, EM clustering, logratio analysis and log-multiplicative (association) analysis. Furthermore, there are specialized distance measures, plotting functions and some helper functions.

There are numerous places to create and download color palettes. These are usually shared in Adobe swatch file formats of some kind. There is also often the need to use standard palettes developed within an organization to ensure that aesthetics are carried over into all projects and output. Now there is a way to read these swatch files in R and avoid transcribing or converting color values by hand or or with other programs. This package provides functions to read and inspect Adobe Color ('ACO'), Adobe Swatch Exchange ('ASE'), GIMP Palette ('GPL'), OpenOffice palette ('SOC') files and KDE Palette ('colors') files. Detailed descriptions of Adobe Color and Swatch Exchange file formats as well as other swatch file formats can be found at <http://www.selapa.net/swatches/colors/fileformats.php>.

Efficient procedures for fitting and cross-validating the structurally-regularized time-dependent Cox models.

Genomic and multi-environmental soybean data. Soybean Nested Association Mapping (SoyNAM) project dataset funded by the United Soybean Board (USB). BLUP function formats data for genome-wide prediction and association analysis.

Automatically replaces "misspelled" words in a character vector based on their string distance from a list of words sorted by their frequency in a corpus. The default word list provided in the package comes from the Corpus of Contemporary American English. Uses the Jaro-Winkler distance metric for string similarity as implemented in van der Loo (2014) <doi:10.32614/RJ-2014-011>. The word frequency data is derived from Davies (2008-) "The Corpus of Contemporary American English (COCA)" <https://www.english-corpora.org/coca/>.

The notion of power index has been widely used in literature to evaluate the influence of individual players (e.g., voters, political parties, nations, stockholders, etc.) involved in a collective decision situation like an electoral system, a parliament, a council, a management board, etc., where players may form coalitions. Traditionally this ranking is determined through numerical evaluation. More often than not however only ordinal data between coalitions is known. The package socialranking offers a set of solutions to rank players based on a transitive ranking between coalitions, including through CP-Majority, ordinal Banzhaf or lexicographic excellence solution summarized by Tahar Allouche, Bruno Escoffier, Stefano Moretti and Meltem à ztürk (2020, <doi:10.24963/ijcai.2020/3>).

This package provides a simple, configurable framework for OpenID Connect (OIDC) authentication and OAuth 2.0 authorization in shiny applications using S7 classes. Defines providers, clients, and tokens, as well as various supporting functions and a shiny module. Features include cross-site request forgery (CSRF) protection, state encryption, Proof Key for Code Exchange (PKCE) handling, validation of OIDC identity tokens (nonces, signatures, claims), automatic user info retrieval for OIDC and supported OAuth providers, asynchronous flows, and hooks for audit logging.

This package creates ggplot2'-based visualizations of smooth effects from GAM (Generalized Additive Models) fitted with mgcv and spline effects from GLM (Generalized Linear Models). Supports survey-weighted models ('svyglm', svycoxph') from the survey package, interaction terms, and provides hazard ratio plots with histograms for survival analysis. Wood (2017, ISBN:9781498728331) provides comprehensive methodology for generalized additive models.

This package provides a fast, GPU-free 3D software renderer written in modern C++17 with native R bindings. Renders triangle meshes to publication-quality images entirely on the CPU, requiring no display server or graphics hardware. Features multi-light Blinn-Phong shading, screen-space ambient occlusion, anti-aliasing, depth fog, transparency, wireframe rendering, texture mapping, screen-space lines and text labels, and procedural geometry generation. Supports standard mesh file formats with PNG and PPM output. Works on high-performance computing clusters, headless servers, containers, and continuous integration pipelines, making it suitable for scientific visualization across neuro-imaging, molecular structures, and general 3D graphics.

Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.

The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" <arXiv:1911.05461>; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory".

This package provides a function that behaves nearly as base::source() but implements a caching mechanism on disk, project based. It allows to quasi source() R scripts that gather data but can fail or consume to much time to respond even if nothing new is expected. It comes with tools to check and execute on demand or when cache is invalid the script.