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.

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>.

This package provides a grammar of data manipulation with data.table', providing a consistent a series of utility functions that help you solve the most common data manipulation challenges.

An implementation of Dcifer (Distance for complex infections: fast estimation of relatedness), an identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data. The package includes functions that format and preprocess the data, implement the method, and visualize the results. Gerlovina et al. (2022) <doi:10.1093/genetics/iyac126>.

Detrend fluorescence microscopy image series for fluorescence fluctuation and correlation spectroscopy ('FCS and FFS') analysis. This package contains functionality published in a 2016 paper <doi:10.1093/bioinformatics/btx434> but it has been extended since then with the Robin Hood algorithm and thus contains unpublished work.

This package provides a suite of functions for analyzing and visualizing the health economic outputs of mathematical models. This package was developed with funding from the National Institutes of Allergy and Infectious Diseases of the National Institutes of Health under award no. R01AI138783. The content of this package is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The theoretical underpinnings of dampack''s functionality are detailed in Hunink et al. (2014) <doi:10.1017/CBO9781139506779>.

This package provides S4 classes and methods for storing dense and sparse matrices in DuckDB databases. The package supports constructing database-backed matrices from base R and Matrix objects, extracting slices and summaries, performing arithmetic and selected linear algebra operations, and materializing results for larger-than-memory workflows. It integrates with dbProject to keep database paths, live connections, and lazy matrix tables synchronized across interactive analyses.

This package provides functions are provided that facilitate the analysis of SNP (single nucleotide polymorphism) data to answer questions regarding captive breeding and relatedness between individuals. dartR.captive is part of the dartRverse suit of packages. Gruber et al. (2018) <doi:10.1111/1755-0998.12745>. Mijangos et al. (2022) <doi:10.1111/2041-210X.13918>.

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>.

Create D3 based SVG ('Scalable Vector Graphics') graphics using a simple R API. The package aims to simplify the creation of many SVG plot types using a straightforward R API. The package relies on the r2d3 R package and the D3 JavaScript library. See <https://rstudio.github.io/r2d3/> and <https://d3js.org/> respectively.

In-line functions for multivariate optimization via desirability functions (Derringer and Suich, 1980, <doi:10.1080/00224065.1980.11980968>) with easy use within dplyr pipelines.

Explore neural networks in a layer oriented way, the framework is intended to give the user total control of the internals of a net without much effort. Use classes like PerceptronLayer to create a layer of Percetron neurons, and specify how many you want. The package does all the tricky stuff internally leaving you focused in what you want. I wrote this package during a neural networks course to help me with the problem set.

This package provides an operator for assigning nested components of a list to names via a concise pattern matching syntax. This is especially convenient for assigning individual names to the multiple values that a function may return in the form of a list, and for extracting deeply nested list components.

Allows for export of DiagrammeR Graphviz objects to SVG.

This package implements a Bayesian Optimal Phase II design (DTE-BOP2) for trials with delayed treatment effects, particularly relevant to immunotherapy studies where treatment benefits may emerge after a delay. The method builds upon the BOP2 framework and incorporates uncertainty in the delay timepoint through a truncated gamma prior, informed by expert knowledge or default settings. Supports two-arm trial designs with functionality for sample size determination, interim and final analyses, and comprehensive simulation under various delay and design scenarios. Ensures rigorous type I and II error control while improving trial efficiency and power when the delay effect is present. A manuscript describing the methodology is under development and will be formally referenced upon publication.

This package provides a convenient framework to simulate, test, power, and visualize data for differential expression studies with lognormal or negative binomial outcomes. Supported designs are two-sample comparisons of independent or dependent outcomes. Power may be summarized in the context of controlling the per-family error rate or family-wise error rate. Negative binomial methods are described in Yu, Fernandez, and Brock (2017) <doi:10.1186/s12859-017-1648-2> and Yu, Fernandez, and Brock (2020) <doi:10.1186/s12859-020-3541-7>.

This package performs an exploratory data analysis through a shiny interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.

Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model.

This package provides the ability to generate images from documents of different types. Three main features are provided: generating document thumbnails, performing visual tests of documents, and updating fields and tables of contents of a Microsoft Word or RTF document. Microsoft Word and/or LibreOffice must be installed on the machine. If Microsoft Word is available, it can produce PDF documents or images identical to the originals; otherwise LibreOffice is used and the rendering may sometimes differ from the original documents.

Containing the Detrended Fluctuation Analysis (DFA), Detrended Cross-Correlation Analysis (DCCA), Detrended Cross-Correlation Coefficient (rhoDCCA), Delta Amplitude Detrended Cross-Correlation Coefficient (DeltarhoDCCA), log amplitude Detrended Fluctuation Analysis (DeltalogDFA), and the Activity Balance Index, it also includes two DFA automatic methods for identifying crossover points and a Deltalog automatic method for identifying reference channels.

Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.

This package performs calculations with tree taper (or stem profile) equations, including model fitting. The package implements the methods from Garcà a, O. (2015) "Dynamic modelling of tree form" <http://mcfns.net/index.php/Journal/article/view/MCFNS7.1_2>. The models are parsimonious, describe well the tree bole shape over its full length, and are consistent with wood formation mechanisms through time.

Interface with the Dat p2p network protocol <https://datproject.org>. Clone archives from the network, share your own files, and install packages from the network.

Fast distributed/parallel estimation for multinomial logistic regression via Poisson factorization and the gamlr package. For details see: Taddy (2015, AoAS), Distributed Multinomial Regression, <doi:10.48550/arXiv.1311.6139>.