This package provides a Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data. It estimates the number of clusters directly from the data using a Dirichlet-process prior. See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. <doi:10.1214/13-AOAS650>.

Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history.

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 documentation in form of a common vignette to packages distr', distrEx', distrMod', distrSim', distrTEst', distrTeach', and distrEllipse'.

This package implements the deflist class, a read-only list-like object that accesses its elements via a function. The deflist class can be used to model deferred access to data or computations by routing indexed list access to a function. This approach is particularly useful when sequential list-like access to data is required but holding all the data in memory at once is not feasible. The package also provides utilities for memoisation and caching to optimize access to frequently requested elements.

This package provides R bindings to the dockview JavaScript library <https://dockview.dev/>. Create fully customizable grid layouts (docks) in seconds to include in interactive R reports with R Markdown or Quarto or in shiny apps <https://shiny.posit.co/>. In shiny mode, modify docks by dynamically adding, removing or moving panels or groups of panels from the server function. Choose among 8 stunning themes (dark and light), serialise the state of a dock to restore it later.

This package provides functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), <DOI:10.1016/j.patrec.2017.08.019>. Performance criteria are added to compare similarity between two signals (query and reference).

Distributed estimation method is based on a Laplace factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.

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>. An empirical comparison of the methods was performed in Stolte et al. (2026) <doi:10.48550/arXiv.2604.11458> for categorical data and in Stolte et al. (2026) <doi:10.48550/arXiv.2604.12327> for numeric data.

This package provides a simple syntax to change the default values for function arguments, whether they are in packages or defined locally.

Model estimation, dispersion testing and diagnosis of hyper-Poisson Saez-Castillo, A.J. and Conde-Sanchez, A. (2013) <doi:10.1016/j.csda.2012.12.009> and Conway-Maxwell-Poisson Huang, A. (2017) regression models.

Base DataSHIELD functions for the client side. DataSHIELD is a software package which allows you to do non-disclosive federated analysis on sensitive data. DataSHIELD analytic functions have been designed to only share non disclosive summary statistics, with built in automated output checking based on statistical disclosure control. With data sites setting the threshold values for the automated output checks. For more details, see citation('dsBaseClient').

The function takes a DNA sequence, a start point, an end point in the sequence, dot size and dot color and draws a fractal image of the sequence. The fractal starts in the center of the canvas. The image is drawn by moving base by base along the sequence and dropping a midpoint between the actual point and the corner designated by the actual base. For more details see Jeffrey (1990) <doi:10.1093/nar/18.8.2163>, Hill, Schisler, and Singh (1992) <doi:10.1007/BF00178602>, and Löchel and Heider (2021) <doi:10.1016/j.csbj.2021.11.008>.

It allows running Dynare program from base R, R Markdown and Quarto. Dynare is a software platform for handling a wide class of economic models, in particular dynamic stochastic general equilibrium ('DSGE') and overlapping generations ('OLG') models. This package does not only integrate R and Dynare but also serves as a Dynare Knit-Engine for knitr package. The package requires Dynare (<https://www.dynare.org/>) and Octave (<https://www.octave.org/download.html>). Write all your Dynare commands in R or R Markdown chunk.

Simulates temporally structured perturbations in matrix population models and computes population reduction and integrated demographic vulnerability across perturbation regimes. Perturbations can be applied to adult survival, juvenile survival, fecundity, all demographic entries, or user-defined matrix elements. The package provides tools to simulate individual perturbation trajectories, evaluate perturbation grids, and summarize demographic vulnerability in structured populations.

Supports propensity score-based methodsâ including matching, stratification, and weightingâ for estimating causal treatment effects. It also implements calibration using negative control outcomes to enhance robustness. debiasedTrialEmulation facilitates effect estimation for both binary and time-to-event outcomes, supporting risk ratio (RR), odds ratio (OR), and hazard ratio (HR) as effect measures. It integrates statistical modeling and visualization tools to assess covariate balance, equipoise, and bias calibration. Additional methodsâ including approaches to address immortal time bias, information bias, selection bias, and informative censoringâ are under development. Users interested in these extended features are encouraged to contact the package authors.

Tests whether multivariate ordinal data may stem from discretizing a multivariate normal distribution. The test is described by Foldnes and Grønneberg (2019) <doi:10.1080/10705511.2019.1673168>. In addition, an adjusted polychoric correlation estimator is provided that takes marginal knowledge into account, as described by Grønneberg and Foldnes (2022) <doi:10.1037/met0000495>.

Access data sets for demonstrating or testing diagnostic classification models. Simulated data sets can be used to compare estimated model output to true data-generating values. Real data sets can be used to demonstrate real-world applications of diagnostic models.

This package provides functions to download, process, and visualize German geospatial data across administrative levels, including states, districts, and municipalities. Supports interactive tables and customized maps using built-in or external datasets. Official shapefiles are accessed from the German Federal Agency for Cartography and Geodesy (BKG) <https://gdz.bkg.bund.de/>, licensed under dl-de/by-2-0 <https://www.govdata.de/dl-de/by-2-0>.

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.

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 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>.

An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC.

Ecological Metadata Language or EML is a long-established format for describing ecological datasets to facilitate sharing and re-use. Because EML is effectively a modified xml schema, however, it is challenging to write and manipulate for non-expert users. delma supports users to write metadata statements in R Markdown or Quarto markdown format, and parse them to EML and (optionally) back again.