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 client-side package which should be installed locally, and is used in conjunction with the server-side 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 a wide collection of univariate discrete data sets from various applied domains related to distribution theory. The functions allow quick, easy, and efficient access to 100 univariate discrete data sets. The data are related to different applied domains, including medical, reliability analysis, engineering, manufacturing, occupational safety, geological sciences, terrorism, psychology, agriculture, environmental sciences, road traffic accidents, demography, actuarial science, law, and justice. The documentation, along with associated references for further details and uses, is presented.

Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.

Includes functions for the construction of matched samples that are balanced and representative by design. Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs. Also, they can be used for the design of randomized experiments, for example, for matching before randomization. By default, designmatch uses the highs optimization solver, but its performance is greatly enhanced by the Gurobi optimization solver and its associated R interface. For their installation, please follow the instructions at <https://www.gurobi.com/getting-started/> and <https://docs.gurobi.com/projects/optimizer/en/current/reference/r/setup.html>. We have also included directions in the gurobi_installation file in the inst folder.

Detection of runs of homozygosity and of heterozygosity in diploid genomes using two methods: sliding windows (Purcell et al (2007) <doi:10.1086/519795>) and consecutive runs (Marras et al (2015) <doi:10.1111/age.12259>).

This package provides an R6-based project container for managing DuckDB connections, reconnecting lazy database tables after session restarts, and storing metadata for database-backed objects used by packages in the dbverse'. The package supplies S4 base classes and generics for database-backed data, helpers for validating DuckDB connections and table names, utilities for creating persistent database views, and methods for writing and restoring pinned lazy tables through the pins package. These tools help package authors and analysts keep database paths, cached connections, and table references synchronized across interactive sessions and project directories.

This package provides tools for detecting, decomposing, and stress-testing temporal drift in repeated binary decision systems. Complements the decisionpaths package by shifting focus from path construction to system-level change over time. Implements five core analytic modules: (1) prevalence drift â did the overall decision rate change over time?; (2) transition drift â did the probability of switching or persisting change?; (3) entropy and stability trends â did path complexity evolve?; (4) group-differential drift â did the system drift differently across subgroups?; (5) change-point and regime-shift detection â did the system change abruptly after a policy or model update? Additionally provides a robustness module for testing stability of drift conclusions across analytic choices, and a sensitivity module for probing vulnerability to data problems including missingness, miscoding, and threshold shifts. Defines four original drift indices: the Decision Drift Index (DDI), Transition Drift Index (TDI), Group Differential Drift (GDD), and Cumulative Drift Burden (CDB). Applications include algorithmic audit, AI governance, education, health, and organisational research.

We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation.

In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. Here we use cyclic shift permutations to identify recurrent copy number alterations in a single cohort or recurrent copy number differences in two cohorts based on a common set of genomic markers. Additional functionality is provided to perform downstream analyses, including the creation of summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al. (2011) <doi:10.1093/bioinformatics/btq717> and leverages the theory developed in Walter et al. (2015) <doi:10.1093/biomet/asv046>. An article describing DiNAMIC.Duo by Walter et al. (2022) can be found at <doi: 10.1093/bioinformatics/btac542>.

The Ditwah storm began impacting Sri Lanka on 25 November 2025. Ditwah provides a collection of tidy, well-structured datasets to support storm data management, monitoring, and early warning applications in Sri Lanka. The publicly available data were converted to tidy data format for easy analysis. The package processes weather data, flood data and situation report data (families affected, etc.). The package also includes functions for analyzing river level progression and load dashboard visualizations to enhance situational awareness. This is also developed for educational purposes to support learning in data wrangling, visualization, and disaster analytics.

Designed to support the visualization, numerical computation, qualitative analysis, model-data fusion, and stochastic simulation for autonomous systems of differential equations. Euler and Runge-Kutta methods are implemented, along with tools to visualize the two-dimensional phaseplane. Likelihood surfaces and a simple Markov Chain Monte Carlo parameter estimator can be used for model-data fusion of differential equations and empirical models. The Euler-Maruyama method is provided for simulation of stochastic differential equations. The package was originally written for internal use to support teaching by Zobitz, and refined to support the text "Exploring modeling with data and differential equations using R" by John Zobitz (2021) <https://jmzobitz.github.io/ModelingWithR/index.html>.

This package provides flexible examples of LLN and CLT for teaching purposes in secondary school.

This package performs distance sampling simulations. dsims repeatedly generates instances of a user defined population within a given survey region. It then generates realisations of a survey design and simulates the detection process. The data are then analysed so that the results can be compared for accuracy and precision across all replications. This process allows users to optimise survey designs for their specific set of survey conditions. The effects of uncertainty in population distribution or parameters can be investigated under a number of simulations so that users can be confident that they have achieved a robust survey design before deploying vessels into the field. The distance sampling designs used in this package from dssd are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). General distance sampling methods are detailed in Introduction to Distance Sampling: Estimating Abundance of Biological Populations, Buckland et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating animal/plant abundance with distance sampling at <https://distancesampling.org/>.

Doubly censored data, as described in Chang and Yang (1987) <doi: 10.1214/aos/1176350608>), are commonly seen in many fields. We use EM algorithm to compute the non-parametric MLE (NPMLE) of the cummulative probability function/survival function and the two censoring distributions. One can also specify a constraint F(T)=C, it will return the constrained NPMLE and the -2 log empirical likelihood ratio for this constraint. This can be used to test the hypothesis about the constraint and, by inverting the test, find confidence intervals for probability or quantile via empirical likelihood ratio theorem. Influence functions of hat F may also be calculated, but currently, the it may be slow.

Several statistical methods for analyzing survival data under various forms of dependent censoring are implemented in the package. In addition to accounting for dependent censoring, it offers tools to adjust for unmeasured confounding factors. The implemented approaches allow users to estimate the dependency between survival time and dependent censoring time, based solely on observed survival data. For more details on the methods, refer to Deresa and Van Keilegom (2021) <doi:10.1093/biomet/asaa095>, Czado and Van Keilegom (2023) <doi:10.1093/biomet/asac067>, Crommen et al. (2024) <doi:10.1007/s11749-023-00903-9>, Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>, Willems et al. (2025) <doi:10.48550/arXiv.2403.11860>, Ding and Van Keilegom (2025) and D'Haen et al. (2025) <doi:10.1007/s10985-025-09647-0>.

This package implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering big data (gaussian mixture models for both multivariate and univariate datasets). This version implements the faster alternative-EM* that expedites convergence via structure based data segregation. The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma, Hasan Kurban, Mehmet Dalkilic (2022) <doi:10.1016/j.softx.2021.100944>. Hasan Kurban, Mark Jenne, Mehmet Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.

This package implements the algorithm described in Jun Li and Alicia T. Lamere, "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes" (Unpublished). Detects not only genes that show different average expressions ("differential expression", DE), but also genes that show different diversities of expressions in different groups ("differentially dispersed", DD). DD genes can be important clinical markers. DiPhiSeq uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise. Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi. (2) Added a function for estimating the proportion of outliers in the data. (3) Modified the input parameter names for clarity, and modified the output format for the main function.

Retrieves code comment decorations for C++ languages of the form \\ [[xyz]]', which are used for automated wrapping of C++ functions.

Based on random forest principle, DynForest is able to include multiple longitudinal predictors to provide individual predictions. Longitudinal predictors are modeled through the random forest. The methodology is fully described for a survival outcome in: Devaux, Helmer, Genuer & Proust-Lima (2023) <doi: 10.1177/09622802231206477>.

This package provides a system for analyzing descriptive representation, especially for comparing the composition of a political body to the population it represents. Users can compute the expected degree of representation for a body under a random sampling model, the expected degree of representation variability, as well as representation scores from observed political bodies. The package is based on Gerring, Jerzak, and Oncel (2024) <doi:10.1017/S0003055423000680>.

Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.

Model-based methods for the detection of disease clusters using GLMs, GLMMs and zero-inflated models. These methods are described in V. Gómez-Rubio et al. (2019) <doi:10.18637/jss.v090.i14> and V. Gómez-Rubio et al. (2018) <doi:10.1007/978-3-030-01584-8_1>.

This package performs the identification of differential risk hotspots (Briz-Redon et al. 2019) <doi:10.1016/j.aap.2019.105278> along a linear network. Given a marked point pattern lying on the linear network, the method implemented uses a network-constrained version of kernel density estimation (McSwiggan et al. 2017) <doi:10.1111/sjos.12255> to approximate the probability of occurrence across space for the type of event specified by the user through the marks of the pattern (Kelsall and Diggle 1995) <doi:10.2307/3318678>. The goal is to detect microzones of the linear network where the type of event indicated by the user is overrepresented.

This package provides statistical functions to aid in the analysis of contemporary and historical corpora. These transparent functions may be useful to anyone, and were designed with the social sciences and humanities in mind. JSD (Jensen-Shannon Divergence) is a measure of the distance between two probability distributions. The JSD and Original JSD functions expand on existing functions, by calculating the JSD for distributions of words in text groups for all pairwise groups provided (Drost (2018) <doi:10.21105/joss.00765>). The Log Likelihood function is inspired by the work of digital historian Jo Guldi (Guldi (2022) <https://github.com/joguldi/digital-history>). Also includes helper functions that can count word frequency in each text grouping, and remove stop words.