This package provides a collection of methods for quantifying the similarity of two or more datasets, many of which can be used for two- or k-sample testing. It provides newly implemented methods as well as wrapper functions for existing methods that enable calling many different methods in a unified framework. The methods were selected from the review and comparison of Stolte et al. (2024) <doi:10.1214/24-SS149>.

To create demographic table with simple summary statistics, with optional comparison(s) over one or more groups.

Work within the dplyr workflow to add random variates to your data frame. Variates can be added at any level of an existing column. Also, bounds can be specified for simulated variates.

This package provides methods to apply decomposition-based relative importance analysis for R functions. This package supports the application of decomposition methods by providing lapply'- or Map'-like meta-functions that compute dominance analysis (Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129>; Grömping, U. (2007) <doi:10.1198/000313007X188252>) an extension of Shapley value regression (Lipovetsky, S., & Conklin, M. (2001) <doi:10.1002/asmb.446>) based on the values returned from other functions.

This package provides functions to run the CRM and TITE-CRM in phase I trials and calibration tools for trial planning purposes.

Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.

Designed for network analysis, leveraging the personalized PageRank algorithm to calculate node scores in a given graph. This innovative approach allows users to uncover the importance of nodes based on a customized perspective, making it particularly useful in fields like bioinformatics, social network analysis, and more.

Extracts colonisation and branching times of island species to be used for analysis in the R package DAISIE'. It uses phylogenetic and endemicity data to extract the separate island colonists and store them.

Implementation of different algorithms for analyzing randomly truncated data, one-sided and two-sided (i.e. doubly) truncated data. It serves to compute empirical cumulative distributions and also kernel density and hazard functions using different bandwidth selectors. Several real data sets are included.

This package creates testthat tests from roxygen examples using simple tags.

Estimate and return the needed parameters for visualizations designed for OpenBudgets.eu <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the OpenBudgets.eu data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to JSON format and use them as input in a different graphical interface.

An implementation of distributional random forests as introduced in Cevid & Michel & Meinshausen & Buhlmann (2020) <doi:10.48550/arXiv.2005.14458>.

This package provides 2D and 3D tour animations as HTML widgets. The user can interact with the widgets using orbit controls, tooltips, brushing, and timeline controls. Linked brushing is supported using crosstalk', and widgets can be embedded in Shiny apps or HTML documents.

Create and evaluate probability distribution objects from a variety of families or define custom distributions. Automatically compute distributional properties, even when they have not been specified. This package supports statistical modeling and simulations, and forms the core of the probaverse suite of R packages.

Generally, most of the packages specify the probability density function, cumulative distribution function, quantile function, and random numbers generation of the probability distributions. The present package allows to compute some important distributional properties, including the first four ordinary and central moments, Pearson's coefficient of skewness and kurtosis, the mean and variance, coefficient of variation, median, and quartile deviation at some parametric values of several well-known and extensively used probability distributions.

This package provides sample size and power calculations when the treatment time-lag effect is present and the lag duration is either homogeneous across the individual subject, or varies heterogeneously from individual to individual within a certain domain and following a specific pattern. The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) <doi:10.1002/sim.7157>.

Analysis of preprocessed dramatic texts, with respect to literary research. The package provides functions to analyze and visualize information about characters, stage directions, the dramatic structure and the text itself. The dramatic texts are expected to be in CSV format, which can be installed from within the package, sample texts are provided. The package and the reasoning behind it are described in Reiter et al. (2017) <doi:10.18420/in2017_119>.

Distributional instrumental variable (DIV) model for estimation of the interventional distribution of the outcome Y under a do intervention on the treatment X. Instruments, predictors and targets can be univariate or multivariate. Functionality includes estimation of the (conditional) interventional mean and quantiles, as well as sampling from the fitted (conditional) interventional distribution.

Compares the fit of alternative models of continuous trait differentiation between sister species and other paired lineages. Differences in trait means between two lineages arise as they diverge from a common ancestor, and alternative processes of evolutionary divergence are expected to leave unique signatures in the distribution of trait differentiation in datasets comprised of many lineage pairs. Models include approximations of divergent selection, drift, and stabilizing selection. A variety of model extensions facilitate the testing of process-to-pattern hypotheses. Users supply trait data and divergence times for each lineage pair. The fit of alternative models is compared in a likelihood framework.

Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) <doi:10.1080/01621459.2012.695674>; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) <doi:10.1111/biom.12676>. Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample.

Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.

This package provides an easy to use implementation of life expectancy decomposition formulas for age bands, derived from Ponnapalli, K. (2005). A comparison of different methods for decomposition of changes in expectation of life at birth and differentials in life expectancy at birth. Demographic Research, 12, pp.141â 172. <doi:10.4054/demres.2005.12.7> In addition, there is a decomposition function for disease cause breakdown and a couple helpful plot functions.

Companion package of Arnaud Barat, Andreu Sansó, Maite Arilla-Osuna, Ruth Blasco, Iñaki Pérez-Fernández, Gabriel Cifuentes-Alcobenda, Rubén Llorente, Daniel Vivar-Rà os, Ella Assaf, Ran Barkai, Avi Gopher, & Jordi Rosell-Ardèvol (2025), "Quantifying Diversity through Entropy Decomposition. Insights into Hominin Occupation and Carcass Processing at Qesem cave".

Curated datasets and intuitive data management functions to streamline epidemiological data workflows. It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, DIVINE makes the process more efficient, transparent, and reproducible.