This package provides a single function that supports the installation of all packages belonging to the dartRverse'. The dartRverse is a set of packages that work together to analyse SNP (single nuclear polymorphism) data. All packages aim to have a similar look and feel and are based on the same type of data structure ('genlight'), with additional metadata for loci and individuals (samples). For more information visit the GitHub pages <https://github.com/green-striped-gecko/dartRverse>.

This package provides a full definition for Weibull tails and Full-Tails Gamma and tools for fitting these distributions to empirical tails. This package build upon the paper by del Castillo, Joan & Daoudi, Jalila & Serra, Isabel. (2012) <doi:10.1017/asb.2017.9>.

The automated clustering and quantification of the digital PCR data is based on the combination of DBSCAN (Hahsler et al. (2019) <doi:10.18637/jss.v091.i01>) and c-means (Bezdek et al. (1981) <doi:10.1007/978-1-4757-0450-1>) algorithms. The analysis is independent of multiplexing geometry, dPCR system, and input amount. The details about input data and parameters are available in the vignette.

The Directed Prediction Index ('DPI') is a causal discovery method for observational data designed to quantify the relative endogeneity of outcome (Y) versus predictor (X) variables in regression models. By comparing the coefficients of determination (R-squared) between the Y-as-outcome and X-as-outcome models while controlling for sufficient confounders and simulating k random covariates, it can quantify relative endogeneity, providing a necessary but insufficient condition for causal direction from a less endogenous variable (X) to a more endogenous variable (Y). Methodological details are provided at <https://psychbruce.github.io/DPI/>. This package also includes functions for data simulation and network analysis (correlation, partial correlation, and Bayesian Networks).

This package performs emulation of dynamic simulators using Gaussian process via one-step ahead approach. The package implements a flexible framework for approximating time-dependent outputs from computationally expensive dynamic systems. It is specifically designed for nonlinear dynamic systems where full simulations may be costly. The underlying Gaussian process model accounts for temporal dependency through the one-step-ahead formulation, allowing for accurate emulation of complex dynamics. Hyperparameters are estimated via maximum likelihood. For methodological details, see Heo (2025, <doi:10.48550/arXiv.2503.20250>) for exact method, and Mohammadi, Challenor, and Goodfellow (2019, <doi:10.1016/j.csda.2019.05.006>) for Monte Carlo method.

This package contains functions for the DivE estimator <doi:10.1371/journal.pcbi.1003646>. The DivE estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.

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 provides a d-statistic tests the null hypothesis of no treatment effect in a matched, nonrandomized study of the effects caused by treatments. A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) <doi:10.1007/978-1-4419-1213-8_14>). There are two examples, one concerning unemployment from Lalive et al. (2006) <doi:10.1111/j.1467-937X.2006.00406.x>, the other concerning smoking and periodontal disease from Rosenbaum (2017) <doi:10.1214/17-STS621>.

This package provides tools for wrapping DataSHIELD analyses into RO-Crate (Research Object Crate) objects. Provides functions to create structured metadata for federated data analysis projects, enabling governance tracking of data access, project membership, analysis execution and output validation across distributed data sources.

An integrated toolset for the analysis of de novo (sporadic) genetic sequence variants. denovolyzeR implements a mutational model that estimates the probability of a de novo genetic variant arising in each human gene, from which one can infer the expected number of de novo variants in a given population size. Observed variant frequencies can then be compared against expectation in a Poisson framework. denovolyzeR provides a suite of functions to implement these analyses for the interpretation of de novo variation in human disease.

Discrete-time multistate models with a user-friendly workflow. The package provides tools for processing data, several ways of estimating parametric and nonparametric multistate models, and an extensive set of Markov chain methods which use transition probabilities derived from the multistate model. Some of the implemented methods are described in Schneider et al. (2024) <doi:10.1080/00324728.2023.2176535>, Dudel (2021) <doi:10.1177/0049124118782541>, Dudel & Myrskylä (2020) <doi:10.1186/s12963-020-00217-0>, van den Hout (2017) <doi:10.1201/9781315374321>.

Read Word documents containing bibliographic references, search for corresponding DOIs using the Crossref API, and append the retrieved DOIs directly to the references. Supports parallel processing for faster retrieval and produces a new Word document with numbered references including DOIs.

This package provides a collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.

This package provides functions to randomly select, return, and print quotes or entire scenes from the American version of the show the Office. Receive laughs from one of of the greatest sitcoms of all time on demand. Add these functions to your .Rprofile to get a good laugh everytime you start a new R session.

Analysis of agreement for nominal data between two raters using the Delta model. This model is proposed as an alternative to the widespread measure Cohen kappa coefficient, which performs poorly when the marginal distributions are very asymmetric (Martin-Andres and Femia-Marzo (2004), <doi:10.1348/000711004849268>; Martin-Andres and Femia-Marzo (2008) <doi:10.1080/03610920701669884>). The package also contains a function to perform a massive analysis of multiple raters against a gold standard. A shiny app is also provided to obtain the measures of nominal agreement between two raters.

Utilities for handling dates and times, such as selecting particular days of the week or month, formatting timestamps as required by RSS feeds, or converting timestamp representations of other software (such as MATLAB and Excel') to R. The package is lightweight (no dependencies, pure R implementations) and relies only on R's standard classes to represent dates and times ('Date and POSIXt'); it aims to provide efficient implementations, through vectorisation and the use of R's native numeric representations of timestamps where possible.

Dominance analysis relative importance methods that are intended for predictive models.

Draw, manipulate, and evaluate directed acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777.

Tests for modality of data using its spacing. The main approach evaluates features (peaks, flats) using a combination of parametric models and non-parametric tests, either after smoothing the spacing by a low-pass filter or by looking over larger intervals.

Nonparametric kernel density estimation, bandwidth selection, and other utilities for analyzing directional data. Implements the estimator in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987) <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in Garcà a-Portugués (2013) <doi:10.1214/13-ejs821>.

Supports global health data analysis, including a publication-ready ggplot2 theme, a flextable defaults helper, a thin pie chart wrapper, built-in regional country-code datasets with a WHO region lookup helper, a geometric mean function for indicator aggregation, and convenience clients for the World Health Organization Global Health Observatory (GHO) OData API <https://ghoapi.azureedge.net/api/> and the United Nations Sustainable Development Goals (SDG) API <https://unstats.un.org/SDGAPI/swagger/>.

This package provides a comprehensive approach for identifying and estimating change points in multivariate time series through various statistical methods. Implements the multiple change point detection methodology from Ryan & Killick (2023) <doi:10.1080/00401706.2023.2183261> and a novel estimation methodology from Fotopoulos et al. (2023) <doi:10.1007/s00362-023-01495-0> generalized to fit the detection methodologies. Performs both detection and estimation of change points, providing visualization and summary information of the estimation process for each detected change point.

This package provides tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.

Estimate common causal parameters using double/debiased machine learning as proposed by Chernozhukov et al. (2018) <doi:10.1111/ectj.12097>. ddml simplifies estimation based on (short-)stacking as discussed in Ahrens et al. (2024) <doi:10.1002/jae.3103>, which leverages multiple base learners to increase robustness to the underlying data generating process.