Scientific and technical article format for the web. Distill articles feature attractive, reader-friendly typography, flexible layout options for visualizations, and full support for footnotes and citations.

This package provides a graphical user interface (GUI) to the functions implemented in the R package DQAstats'. Publication: Mang et al. (2021) <doi:10.1186/s12911-022-01961-z>.

Allows to visualize high-density electroencephalography (HD-EEG) data through interactive plots and animations, enabling exploratory and communicative analysis of temporal-spatial brain signals. Funder: Masaryk University (Grant No. MUNI/A/1457/2023).

Works as an "add-on" to packages like shiny', future', as well as rlang', and provides utility functions. Just like dipping sauce adding flavors to potato chips or pita bread, dipsaus for data analysis and visualizations adds handy functions and enhancements to popular packages. The goal is to provide simple solutions that are frequently asked for online, such as how to synchronize shiny inputs without freezing the app, or how to get memory size on Linux or MacOS system. The enhancements roughly fall into these four categories: 1. shiny input widgets; 2. high-performance computing using the future package; 3. modify R calls and convert among numbers, strings, and other objects. 4. utility functions to get system information such like CPU chip-set, memory limit, etc.

Fits Bayesian copula vector autoregressive models for bivariate time series with dynamic, regime-switching, and constant dependence structures. The package includes simulation, data preparation, estimation with Stan through rstan or cmdstanr', posterior summaries, diagnostics, trajectory extraction, fitted and predictive summaries, and approximate leave-one-out cross-validation model comparison for supported fits. For Bayesian computation and model comparison, see Carpenter et al. (2017) <doi:10.18637/jss.v076.i01> and Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>.

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.

Solves ordinary and delay differential equations, where the objective function is written in either R or C. Suitable only for non-stiff equations, the solver uses a Dormand-Prince method that allows interpolation of the solution at any point. This approach is as described by Hairer, Norsett and Wanner (1993) <ISBN:3540604529>. Support is also included for iterating difference equations.

Analysis, visualisation and simulation of digital polymerase chain reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). Supports data formats of commercial systems (Bio-Rad QX100 and QX200; Fluidigm BioMark) and other systems.

Estimation of heterogeneity-robust difference-in-differences estimators, with a binary, discrete, or continuous treatment, in designs where past treatments may affect the current outcome.

The distributed expectation maximization algorithms are used to solve parameters of multivariate Gaussian mixture models. The philosophy of the package is described in Guo, G. (2022) <doi:10.1080/02664763.2022.2053949>.

Collection of functions for fitting and interpreting distributed lag interaction models (DLIM). A DLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a continuous variable. Includes a dlim() function for fitting, predict() function for inference, and plotting functions for visualization. Details on methodology are described in Demateis et al. (2024) <doi:10.1002/env.2843>.

This hosts the findRFM function which generates RFM scores on a 1-5 point scale for customer transaction data. The function consumes a data frame with Transaction Number, Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes. The function returns a data frame with RFM data for the sales information.

Scripting of structural equation models via lavaan for Dyadic Data Analysis, and helper functions for supplemental calculations, tabling, and model visualization.

This package provides a set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. Each method offers automatic estimation, but users may also specify exact parameters or use a graphical interface to guess parameters in the traditional way if desired.

Computes pathway-level transcriptional disruption scores from differential expression p-values using permutation tests with Generalized Pareto Distribution (GPD) tail extrapolation and false discovery rate (FDR) correction. Reference: Guo P, Li H (2026) DSGE: Disruption Score of Gene Expression for Gene-Set Enrichment Analysis. <https://github.com/LHJLab/DSGE>.

Offers statistical methods to compare diagnostic performance between two binary diagnostic tests on the same subject in clinical studies. Includes functions for generating formatted tables to display diagnostic outcomes, facilitating a clear and comprehensive comparison directly through the R console. Inspired by and extending the functionalities of the DTComPair', tableone', and gtsummary packages.

Allows clinicians and researchers to compute daily dose (and subsequently days supply) for prescription refills using the following methods: Fixed window, fixed tablet, defined daily dose (DDD), and Random Effects Warfarin Days Supply (REWarDS). Daily dose is the computed dose that the patient takes every day. For medications with fixed dosing (e.g. direct oral anticoagulants) this is known and does not need to be estimated. For medications with varying dose such as warfarin, however, the daily dose should be assumed or estimated to allow measurement of drug exposure. Daysâ supply is the number of days that patientsâ supply of medication will last after each prescription fill. Estimating daysâ supply is necessary to calculate drug exposure. The package computes daysâ supply and daily dose at both the prescription and patient levels. Results at the prescription level are denoted with â -Rx-â and those at patient level are denoted with â -Pt-â .

Create donut charts positioned on maps from tidy data. The package provides helpers to compute donut polygon geometries, optional origin-destination flow lines, ready-to-use ggplot2 map layers, and interactive leaflet widgets.

Nonparametric efficiency measurement by data envelopment analysis. Provides radial (Charnes-Cooper-Rhodes and Banker-Charnes-Cooper) technical efficiency under constant, variable, non-increasing and non-decreasing returns to scale, the slacks-based measure of Tone (2001), the additive model of Charnes and others (1985), and the directional distance function of Chambers, Chung and Fare (1996), all through one interface and one result object. Efficiency estimates are accompanied by peers, slacks, returns-to-scale classification, scale efficiency and the optimal multipliers, and by bias-corrected estimates and confidence intervals from the smoothed homogeneous bootstrap of Simar and Wilson (1998). Where prices are known, cost, revenue and Nerlovian profit efficiency separate the technical component from the allocative one; where they are not, cross-efficiency with the secondary goals of Doyle and Green (1994) ranks units that a self-appraisal leaves tied. This package succeeds the archived DEA package of Diaz-Martinez and Fernandez-Menendez (2008).

Diff, patch and merge for data frames. Document changes in data sets and use them to apply patches. Changes to data can be made visible by using render_diff(). The V8 package is used to wrap the daff.js JavaScript library which is included in the package.

Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.

Extends daltoolbox with Python-backed components for deep learning, scikit-learn classification, and time-series forecasting through reticulate'. The package provides objects that follow the daltoolbox architecture while delegating model creation, fitting, encoding, and prediction to Python libraries such as torch and scikit-learn'. In the package name, dp stands for Deep Python'. The overall workflow is inspired by the Experiment Lines approach described in Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.

This package provides an interface to D4Science StorageHub API (<https://dev.d4science.org/>). Allows to get user profile, and perform actions over the StorageHub (workspace) including creation of folders, files management (upload/update/deletion/sharing), and listing of stored resources.

Cluster-randomized trials (CRTs) assign treatment to groups rather than individuals, so valid analyses must distinguish cluster-level and individual-level effects and define estimands within a potential-outcomes framework. This package supports right-censored survival outcomes for both single-state (binary) and multi-state settings. For single-state outcomes, it provides estimands based on stage-specific survival contrasts (SPCE) and restricted mean survival time (RMST). For multi-state outcomes, it provides SPCE as well as a generalized win-based restricted mean time-in-favor estimand (RMT-IF). The package implements doubly robust estimators that accommodate covariate-dependent censoring and remain consistent if either the outcome model or the censoring model is correctly specified. Users can choose marginal Cox or gamma-frailty Cox working models for nuisance estimation, and inference is supported via leave-one-cluster-out jackknife variance and confidence interval estimation. Methods are described in Fang et al. (2025) "Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes" <doi:10.48550/arXiv.2510.08438>.