Formatting of population and case data, calculation of Standardized Incidence Ratios, and fitting the BYM model using INLA'. For details see Brown (2015) <doi:10.18637/jss.v063.i12>.

This dataset includes Background and Pathway data used in package DysPIA'.

An implementation of distributional random forests as introduced in Cevid & Michel & Naf & Meinshausen & Buhlmann (2022) <doi:10.48550/arXiv.2005.14458>.

Implementation of new discrete statistical distributions. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters within the gamlss framework.

Bayesian Beta Regression, adapted for bounded discrete responses, commonly seen in survey responses. Estimation is done via Markov Chain Monte Carlo sampling, using a Gibbs wrapper around univariate slice sampler (Neal (2003) <DOI:10.1214/aos/1056562461>), as implemented in the R package MfUSampler (Mahani and Sharabiani (2017) <DOI: 10.18637/jss.v078.c01>).

Distributed Online Mean Tests is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform mean tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, Domean ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for mean testing. The philosophy of Domean is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.

Mimics the demo functionality for Shiny apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic).

S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).

Estimates Two-way Fixed Effects difference-in-differences/event-study models using the imputation-based approach proposed by Borusyak, Jaravel, and Spiess (2021).

This package provides tools to estimate and manage empirical distributions, which should work with survey data. One of the main features is the possibility to create data cubes of estimated statistics, that include all the combinations of the variables of interest (see for example functions dcc5() and dcc6()).

Package to fit diffusion-based IRT models to response and response time data. Models are fit using marginal maximum likelihood. Parameter restrictions (fixed value and equality constraints) are possible. In addition, factor scores (person drift rate and person boundary separation) can be estimated. Model fit assessment tools are also available. The traditional diffusion model can be estimated as well.

Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.

Implementation of frequency tables and bar charts for qualitative variables and checkbox fields. This package implements tables and charts used in reports at Funarte (National Arts Foundation) and OBEC (Culture and Creative Economy Observatory) in Brazil, and its main purpose is to simplify the use of R for people with a background in the humanities and arts. Examples and details can be viewed in this presentation from 2026: <https://formacao2026.netlify.app/assets/modulo_3/modulo3#/title-slide>.

Efficient covariate-adjusted estimators of quantities that are useful for establishing the effects of treatments on ordinal outcomes.

Implementation of selected Tidyverse functions within DataSHIELD', an open-source federated analysis solution in R. Currently, DataSHIELD contains very limited tools for data manipulation, so the aim of this package is to improve the researcher experience by implementing essential functions for data manipulation, including subsetting, filtering, grouping, and renaming variables. This is the clientside package which should be installed locally, and is used in conjuncture with the serverside package dsTidyverse which is installed on the remote server holding the data. For more information, see <https://tidyverse.org/> and <https://datashield.org/>.

This package provides tools for exploring the topography of 3d triangle meshes. The functions were developed with dental surfaces in mind, but could be applied to any triangle mesh of class mesh3d'. More specifically, doolkit allows to isolate the border of a mesh, or a subpart of the mesh using the polygon networks method; crop a mesh; compute basic descriptors (elevation, orientation, footprint area); compute slope, angularity and relief index (Ungar and Williamson (2000) <https://palaeo-electronica.org/2000_1/gorilla/issue1_00.htm>; Boyer (2008) <doi:10.1016/j.jhevol.2008.08.002>), inclination and occlusal relief index or gamma (Guy et al. (2013) <doi:10.1371/journal.pone.0066142>), OPC (Evans et al. (2007) <doi:10.1038/nature05433>), OPCR (Wilson et al. (2012) <doi:10.1038/nature10880>), DNE (Bunn et al. (2011) <doi:10.1002/ajpa.21489>; Pampush et al. (2016) <doi:10.1007/s10914-016-9326-0>), form factor (Horton (1932) <doi:10.1029/TR013i001p00350>), basin elongation (Schum (1956) <doi:10.1130/0016-7606(1956)67[597:EODSAS]2.0.CO;2>), lemniscate ratio (Chorley et al; (1957) <doi:10.2475/ajs.255.2.138>), enamel-dentine distance (Guy et al. (2015) <doi:10.1371/journal.pone.0138802>; Thiery et al. (2017) <doi:10.3389/fphys.2017.00524>), absolute crown strength (Schwartz et al. (2020) <doi:10.1098/rsbl.2019.0671>), relief rate (Thiery et al. (2019) <doi:10.1002/ajpa.23916>) and area-relative curvature; draw cumulative profiles of a topographic variable; and map a variable over a 3d triangle mesh.

Implementation of a transfer learning framework employing distribution mapping based domain transfer. Uses the renowned concept of histogram matching (see Gonzalez and Fittes (1977) <doi:10.1016/0094-114X(77)90062-3>, Gonzalez and Woods (2008) <isbn:9780131687288>) and extends it to include distribution measures like kernel density estimates (KDE; see Wand and Jones (1995) <isbn:978-0-412-55270-0>, Jones et al. (1996) <doi:10.2307/2291420). In the typical application scenario, one can use the underlying sample distributions (histogram or KDE) to generate a map between two distinct but related domains to transfer the target data to the source domain and utilize the available source data for better predictive modeling design. Suitable for the case where a one-to-one sample matching is not possible, thus one needs to transform the underlying data distribution to utilize the more available data for modeling.

Fast, flexible and user-friendly tools for distribution comparison through direct density ratio estimation. The estimated density ratio can be used for covariate shift adjustment, outlier-detection, change-point detection, classification and evaluation of synthetic data quality. The package implements multiple non-parametric estimation techniques (unconstrained least-squares importance fitting, ulsif(), Kullback-Leibler importance estimation procedure, kliep(), spectral density ratio estimation, spectral(), kernel mean matching, kmm(), and least-squares hetero-distributional subspace search, lhss()). with automatic tuning of hyperparameters. Helper functions are available for two-sample testing and visualizing the density ratios. For an overview on density ratio estimation, see Sugiyama et al. (2012) <doi:10.1017/CBO9781139035613> for a general overview, and the help files for references on the specific estimation techniques.

This package creates discretised versions of continuous distribution functions by mapping continuous values to an underlying discrete grid, based on a (uniform) frequency of discretisation, a valid discretisation point, and an integration range. For a review of discretisation methods, see Chakraborty (2015) <doi:10.1186/s40488-015-0028-6>.

This package performs parametric and non-parametric estimation and simulation of drifting semi-Markov processes. The definition of parametric and non-parametric model specifications is also possible. Furthermore, three different types of drifting semi-Markov models are considered. These models differ in the number of transition matrices and sojourn time distributions used for the computation of a number of semi-Markov kernels, which in turn characterize the drifting semi-Markov kernel. For the parametric model estimation and specification, several discrete distributions are considered for the sojourn times: Uniform, Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric model specification makes no assumptions about the shape of the sojourn time distributions. Semi-Markov models are described in: Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>. Drifting Markov models are described in: Vergne, N. (2008) <doi:10.2202/1544-6115.1326>. Reliability indicators of Drifting Markov models are described in: Barbu, V. S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8>. We acknowledge the DATALAB Project <https://lmrs-num.math.cnrs.fr/projet-datalab.html> (financed by the European Union with the European Regional Development fund (ERDF) and by the Normandy Region) and the HSMM-INCA Project (financed by the French Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005).

Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.

Detection and attribution of climate change using methods including optimal fingerprinting via generalized total least squares or an estimating equation approach (Li et al., 2025, <doi:10.1175/JCLI-D-24-0193.1>; Ma et al., 2023, <doi:10.1175/JCLI-D-22-0681.1>). Provides shrinkage estimators for the covariance matrix following Ledoit and Wolf (2004, <doi:10.1016/S0047-259X(03)00096-4>) and Ledoit and Wolf (2017, <doi:10.2139/ssrn.2383361>).

Build donut/pie charts with ggplot2 layer by layer, exploiting the advantages of polar symmetry. Leverage layouts to distribute labels effectively. Connect labels to donut segments using pins. Streamline annotation and highlighting.

Create and manage fault-tolerant task queues for the foreach package using the Redis key/value database.