This package provides a set of fast, chainable image-processing operations which are applicable to images of two, three or four dimensions, particularly medical images.

Introduces in-sample, out-of-sample, pseudo out-of-sample, and benchmark model forecast tests and a new class for working with forecast data, Forecast.

Predicts ploidal level from sequence data using site-based heterozygosity and a mixture models approach. See Gaynor et al. (2024) <doi:10.1002/aps3.11606>.

Confidence intervals and point estimation for R under various parametric model assumptions; likelihood inference based on classical first-order approximations and higher-order asymptotic procedures.

Create PX-files from scratch or read and modify existing ones. Includes a function for every PX keyword, making metadata manipulation simple and human-readable.

Performant interactive scatterplot for ~ 1 million points. Zoom, pan, and pick points. Includes tooltips, labels, a grid overlay, legend, and coupled interactions across multiple plots.

This package provides functions for obtaining the density, random deviates and maximum likelihood estimates of the Poisson lognormal distribution and the bivariate Poisson lognormal distribution.

Fast and Accurate Randomized Singular Value Decomposition (RSVD) methods proposed in the PCAone paper by Li (2023) <https://genome.cshlp.org/content/33/9/1599>.

Compare directories flexibly (by date, content, or both) and synchronize files efficiently, with asymmetric and symmetric modes, helper tools, and visualization support for file management.

Probability functions, family for glm() and Stan code for working with the unifed distribution (Quijano Xacur, 2019; <doi:10.1186/s40488-019-0102-6>).

This package provides R functions to draw lines and curves with the width of the curve allowed to vary along the length of the curve.

This package provides a convenience wrapper that uses the rmarkdown package to render small snippets of code to target formats that include both code and output. The goal is to encourage the sharing of small, reproducible, and runnable examples on code-oriented websites or email. reprex also extracts clean, runnable R code from various common formats, such as copy/paste from an R session.

Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) <doi:10.18637/jss.v103.i13>. Further discussion on robust mediation analysis can be found in Alfons & Schley (2025) <doi:10.1002/wics.70051>.

Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C.

rTorrent is a BitTorrent client with an ncurses interface. It supports full encryption, DHT, PEX, and Magnet Links. It can also be controlled via XML-RPC over SCGI.

This package provides utilities for working with Google APIs. This includes functions and classes for handling common credential types and for preparing, executing, and processing HTTP requests.

This is a package for mixed variable optimization for non-linear functions. It can optimize function whose inputs are a combination of continuous, ordered, and unordered variables.

This package provides functionality to dynamically define R functions and S4 methods with inlined C, C++ or Fortran code supporting .C and .Call calling conventions.

This package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.

This package provides tools for the estimation of indicators on social exclusion and poverty, as well as an implementation of Pareto tail modeling for empirical income distributions.

This package lets you download Google fonts and generate CSS to use in rmarkdown documents and Shiny applications. Some popular fonts are included and ready to use.

Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs where few positives are available for the class being predicted.

Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions.

Simplifies aspects of linear regression analysis, particularly simultaneous inference. Additionally, supports "A Progressive Introduction to Linear Models" by Joshua French (<https://jfrench.github.io/LinearRegression/>).