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.

Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. These allow faceted, coloured displays of quantitative information for comparison across District Health Boards or Regional Councils. The preprint Lumley (2019) <arXiv:1912.04435> is based on the methods in this package.

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.

This package provides a wrapper for the DeepL API <https://developers.deepl.com/docs>, a web service for translating texts between different languages. A DeepL API developer account is required to use the service (see <https://www.deepl.com/pro#developer>).

This tool is for parsing public drug databases such as DrugBank XML database <https://go.drugbank.com/>. The parsed data are then returned in a proper R object called dvobject'.

Statistical tests and test statistics to identify events in a dataset that are dragon kings (DKs). The statistical methods in this package were reviewed in Wheatley & Sornette (2015) <doi:10.2139/ssrn.2645709>.

This is the core package that provides both the user API and developer API to deploy the parallel cluster on the cloud using the container service. The user can call clusterPreset() to define the cloud service provider and container and makeDockerCluster() to create the cluster. The developer should see "developer's cookbook" on how to define the cloud provider and container.

This package provides functionality for users who are learning R or the techniques of data analysis. Written as a collection of wrapper functions, the DTwrapper package facilitates many core operations of data processing. This is achieved with relatively few requirements about the order of the processing steps or knowledge of specialized syntax. DTwrappers creates coding results along with translations to data.table's code. This enables users to benefit from the speed and efficiency of data.table's calculations. Furthermore, the package also provides the translated code for educational purposes so that users can review working examples of coding syntax and calculations.

This package provides a system for the management, assessment, and psychometric analysis of data from educational and psychological tests.

An interface to DifferentialEquations.jl <https://diffeq.sciml.ai/dev/> from the R programming language. It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE), delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality, including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods. Supports GPUs, with support for CUDA (NVIDIA), AMD GPUs, Intel oneAPI GPUs, and Apple's Metal (M-series chip GPUs). diffeqr attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information, see Rackauckas and Nie (2017) <doi:10.5334/jors.151>.

This package creates the "table one" of bio-medical papers. Fill it with your data and the name of the variable which you'll make the group(s) out of and it will make univariate, bivariate analysis and parse it into HTML. It also allows you to visualize all your data with graphic representation.

This package contains data organized by topics: categorical data, regression model, means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA.

Uses the delta-method to estimate the Potential Impact Fraction (PIF) and the Population Attributable Fraction (PAF) from summary data. It creates point-estimates, confidence intervals, and estimates of the variance. Provides an extension to the aggregated data method in Chan, Zepeda-Tello et al (2025) <doi:10.1002/sim.70214>.

Probability mass function, distribution function, quantile function, random generation and parameter estimation for the type I and III discrete Weibull distributions.

Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2024) <doi:10.1016/j.alcr.2024.100617>. The package supports joint modeling of multiple measurements per individual, time-varying and time-invariant effects, and a wide range of discrete and continuous distributions. Estimation of these dynamic multivariate panel models is carried out via Stan'. For an in-depth tutorial of the package, see (Tikka and Helske, 2025) <doi:10.18637/jss.v115.i05>.

Given an initial set of points, this package minimizes the number of elements to discard from this set such that there exists at least one monotonic and convex mapping within pre-specified upper and lower bounds.

Various diffusion models to forecast new product growth. Currently the package contains Bass, Gompertz, Gamma/Shifted Gompertz and Weibull curves. See Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.

It is a novel tool used to identify the candidate drugs against a particular disease based on the drug target set enrichment analysis. It assumes the most effective drugs are those with a closer affinity in the protein-protein interaction network to the specified disease. (See Gómez-Carballa et al. (2022) <doi: 10.1016/j.envres.2022.112890> and Feng et al. (2022) <doi: 10.7150/ijms.67815> for disease expression profiles; see Wishart et al. (2018) <doi: 10.1093/nar/gkx1037> and Gaulton et al. (2017) <doi: 10.1093/nar/gkw1074> for drug target information; see Kanehisa et al. (2021) <doi: 10.1093/nar/gkaa970> for the details of KEGG database.).

Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms.

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>.

Utilities for mixed frequency data. In particular, use to aggregate and normalize tabular mixed frequency data, index dates to end of period, and seasonally adjust tabular data.

This package provides a data augmentation based sampler for conducting privacy-aware Bayesian inference. The dapper_sample() function takes an existing sampler as input and automatically constructs a privacy-aware sampler. The process of constructing a sampler is simplified through the specification of four independent modules, allowing for easy comparison between different privacy mechanisms by only swapping out the relevant modules. Probability mass functions for the discrete Gaussian and discrete Laplacian are provided to facilitate analyses dealing with privatized count data. The output of dapper_sample() can be analyzed using many of the same tools from the rstan ecosystem. For methodological details on the sampler see Ju et al. (2022) <doi:10.48550/arXiv.2206.00710>, and for details on the discrete Gaussian and discrete Laplacian distributions see Canonne et al. (2020) <doi:10.48550/arXiv.2004.00010>.

Create high-performance clinical reporting tables (TLGs) from ADaM-like inputs. The package provides a consistent, programmatic API to generate common tables such as demographics, adverse event incidence, and laboratory summaries, using data.table for fast aggregation over large populations. Functions support flexible target-variable selection, stratification by treatment, and customizable summary statistics, and return tidy, machine-readable results ready to render with downstream table/formatting packages in analysis pipelines.

DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples) builds an ensemble of J48 trees by recursively adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005) <DOI:10.1016/j.inffus.2004.04.001>").