Track and document dplyr data pipelines. As you filter, mutate, and join your way through a data set, dtrackr seamlessly keeps track of your data flow and makes publication ready documentation of a data pipeline simple.

This package provides functions for interacting with all sections of the official Danish Address Web API (also known as DAWA') <https://api.dataforsyningen.dk>. The development of this package is completely independent from the government agency, Klimadatastyrelsen, who maintains the API.

Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of data.table efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.

Interface to the python package dgpsi for Gaussian process, deep Gaussian process, and linked deep Gaussian process emulations of computer models and networks using stochastic imputation (SI). The implementations follow Ming & Guillas (2021) <doi:10.1137/20M1323771> and Ming, Williamson, & Guillas (2023) <doi:10.1080/00401706.2022.2124311> and Ming & Williamson (2023) <doi:10.48550/arXiv.2306.01212>. To get started with the package, see <https://mingdeyu.github.io/dgpsi-R/>.

This package provides fast, in-memory reading of DATASUS DBC files using native C code, along with a catalog of public health data sources, FTP file discovery, caching downloads, and a high-level datasus_fetch() function that lists, downloads, and reads files in a single call. Bundles the blast decompressor from zlib contrib/blast to decode PKWare DCL compressed DBC files and parses DBF records directly for efficient import into tibbles. See the DATASUS file transfer site <https://datasus.saude.gov.br> and Adler (2003) <https://github.com/madler/zlib/tree/master/contrib/blast> for details on the underlying data and compression format.

This package provides interactive workshops for learning R easily and happily. Each workshop is a self-contained Quarto Live document whose code cells run in the browser via WebAssembly', so learners can read the instructions and run the exercises side by side without a local R setup. The materials accompany the "Learning R with Dr. Hu" workshop series.

Estimation of DIFferential COexpressed NETworks using diverse and user metrics. This package is basically used for three functions related to the estimation of differential coexpression. First, to estimate differential coexpression where the coexpression is estimated, by default, by Spearman correlation. For this, a metric to compare two correlation distributions is needed. The package includes 6 metrics. Some of them needs a threshold. A new metric can also be specified as a user function with specific parameters (see difconet.run). The significance is be estimated by permutations. Second, to generate datasets with controlled differential correlation data. This is done by either adding noise, or adding specific correlation structure. Third, to show the results of differential correlation analyses. Please see <http://bioinformatica.mty.itesm.mx/difconet> for further information.

An interactive image editing tool that can be added as part of the HTML in Shiny, R markdown or any type of HTML document. Often times, plots, photos are embedded in the web application/file. drawer can take screenshots of these image-like elements, or any part of the HTML document and send to an image editing space called canvas to allow users immediately edit the screenshot(s) within the same document. Users can quickly combine, compare different screenshots, upload their own images and maybe make a scientific figure.

Analysis and visualization of dropout between conditions in surveys and (online) experiments. Features include computation of dropout statistics, comparing dropout between conditions (e.g. Chi squared), analyzing survival (e.g. Kaplan-Meier estimation), comparing conditions with the most different rates of dropout (Kolmogorov-Smirnov) and visualizing the result of each in designated plotting functions. Article published in _Behavior Research Methods_ on dropR by the authors: Dropout analysis: A method for data from Internet-based research and dropR', an R-based web app and package to analyze and visualize dropout. (2025) <doi:10.3758/s13428-025-02730-2>. Sources: Andrea Frick, Marie-Terese Baechtiger & Ulf-Dietrich Reips (2001) <doi:10.5167/uzh-19758>; Ulf-Dietrich Reips (2002) <doi:10.1026//1618-3169.49.4.243>.

An RStudio addin for teaching and learning data manipulation using the dplyr package. You can learn each steps of data manipulation by clicking your mouse without coding. You can get resultant data (as a tibble') and the code for data manipulation.

Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own.

This package provides extra functions to manipulate dendrograms that build on the base functions provided by the stats package. The main functionality it is designed to add is the ability to colour all the edges in an object of class dendrogram according to cluster membership i.e. each subtree is coloured, not just the terminal leaves. In addition it provides some utility functions to cut dendrogram and hclust objects and to set/get labels.

This package provides a Scannerless GLR parser/parser generator. Note that GLR standing for "generalized LR", where L stands for "left-to-right" and R stands for "rightmost (derivation)". For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita (1987) algorithm. (Paper can be found at <https://aclanthology.org/P84-1073.pdf>). The original dparser package documentation can be found at <https://dparser.sourceforge.net/>. This allows you to add mini-languages to R (like rxode2's ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like NONMEM to automatically translate them to R code. To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include <dparse.h> use #include <dparser.h>. This also provides a R-based port of the make_dparser <https://dparser.sourceforge.net/d/make_dparser.cat> command called mkdparser(). Additionally you can parse an arbitrary grammar within R using the dparse() function, which works on most OSes and is mainly for grammar testing. The fastest parsing, of course, occurs at the C level, and is suggested.

Concept drift refers to the change in the data distribution or in the relationships between variables over time. drifter calculates distances between variable distributions or variable relations and identifies both types of drift. Key functions are: calculate_covariate_drift() checks distance between corresponding variables in two datasets, calculate_residuals_drift() checks distance between residual distributions for two models, calculate_model_drift() checks distance between partial dependency profiles for two models, check_drift() executes all checks against drift. drifter is a part of the DrWhy.AI universe (Biecek 2018) <arXiv:1806.08915>.

Create disposable R packages for testing. You can create, install and load multiple R packages with a single function call, and then unload, uninstall and destroy them with another function call. This is handy when testing how some R code or an R package behaves with respect to other packages.

Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.

Estimates latent variables of public opinion cross-nationally and over time from sparse and incomparable survey data. DCPO uses a population-level graded response model with country-specific item bias terms. Sampling is conducted with Stan'. References: Solt (2020) <doi:10.31235/osf.io/d5n9p>.

Implementation of the Dirichlet Random Forest algorithm for compositional response data. Supports maximum likelihood estimation ('MLE') and method-of-moments ('MOM') parameter estimation for the Dirichlet distribution. Provides two prediction strategies; averaging-based predictions (average of responses within terminal nodes) and parameter-based predictions (expected value derived from the estimated Dirichlet parameters within terminal nodes). For more details see Masoumifard, van der Westhuizen, and Gardner-Lubbe (2026, ISBN:9781032903910).

This package provides a specific and comprehensive framework for the analyses of time-to-event data in agriculture. Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 <doi:10.1017/wsc.2022.8>.

This package provides a comprehensive framework for early epidemic detection through school absenteeism surveillance. The package offers three core functionalities: (1) simulation of population structures, epidemic spread, and resulting school absenteeism patterns; (2) implementation of surveillance models that generate alerts for impending epidemics based on absenteeism data and (3) evaluation of alert timeliness and accuracy through alert time quality metrics to optimize model parameters. These tools enable public health officials and researchers to develop and assess early warning systems before implementation. Methods are based on research published in Vanderkruk et al. (2023) <doi:10.1186/s12889-023-15747-z> and Ward et al. (2019) <doi:10.1186/s12889-019-7521-7>.

Go beyond standard probability distributions such as the Normal or Exponential by combining, shifting, maximizing, and otherwise transforming distributions with simple, verb-based functions. Provides easy access to a broader space of distributions more representative of real-world systems such as river flows or insurance claims. Part of the probaverse framework of packages to support advanced statistical modeling and simulations with an intuitive workflow.

This package provides methods to compute verifiable false discovery rate (FDR) diagnostic checks for workflows based on target-decoy competition and related confidence measures. Implements calibration, stability and tail diagnostics, including tail support, threshold elasticity, posterior error probability (PEP) reliability, and equal-chance checks. If you used this package in your research, please cite the associated preprint <doi:10.64898/2026.04.16.718468>. Detailed examples of using this package can also be found on the GitHub repository (<https://github.com/Jacky11/diagFDR>).

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

This package implements various decision support tools related to the Econometrics & Technometrics. Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, network DEA, dynamic DEA, intertemporal budgeting, etc.