The aim of the ggplot2 package is to aid in visual data investigations. This focus has led to a lack of facilities for composing specialized plots. This package aims to be a collection of mainly new statistics and geometries that fills this gap.

This package provides a micro-package for reading "passwords", i.e. reading user input with masking, so that the input is not displayed as it is typed. Currently, RStudio, the command line (every OS), and any platform where tcltk is present are supported.

This package provides helper functions to install and maintain the LaTeX distribution named TinyTeX, a lightweight, cross-platform, portable, and easy-to-maintain version of TeX Live. This package also contains helper functions to compile LaTeX documents, and install missing LaTeX packages automatically.

This package provides S3 classes and methods for one-dimensional normal mixture models, for, e.g., density estimation or clustering algorithms research and teaching; it provides the widely used Marron-Wand densities. It also provides tools for efficient random number generation and graphics.

Thin is a Ruby web server that glues together 3 Ruby libraries:

• the Mongrel parser,

• Event Machine, a network I/O library with high scalability, performance and stability,

• Rack, a minimal interface between webservers and Ruby frameworks.

The Ristretto Image Viewer is an application that can be used to view, and scroll through images. It can be used to run a slideshow of images, open images with other applications like an image-editor or configure an image as the desktop wallpaper.

Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available.

This package provides tools for manipulating paired ranges and working with Hi-C data in R. Functionality includes manipulating/merging paired regions, generating paired ranges, extracting/aggregating interactions from `.hic` files, and visualizing the results. Designed for compatibility with plotgardener for visualization.

This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor.

This package provides probability computation, data generation, and model estimation for fully-visible Boltzmann machines. It follows the methods described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.

This package provides a registry of APIs listed on <https://bund.dev> and a core OpenAPI client layer to explore specs and perform requests. Adapter helpers return tidy data frames for supported APIs, with optional response caching and rate limiting guidance.

This package provides a computational toolbox designed for handling missing values in large datasets with the Multiple Imputation by Chained Equations (MICE) by using Apache Spark'. The methodology is described in Morvan et al. (2026) <doi:10.48550/arXiv.2601.21613>.

An R implementation and enhancement of the Dynamic TOPMODEL semi-distributed hydrological model originally proposed by Beven and Freer (2001) <doi:10.1002/hyp.252>. The dynatop package implements code for simulating models which can be created using the dynatopGIS package.

Generate motivational quotes and Shakespearean word combinations (bardâ bits) that a user can consider for their personal projects. Each of the package functions takes two arguments, cat which default to any, and a a numeric or character seed to ensure reproducible results.

Computes shrinkage estimators for regression problems. Selects penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution. See Keller and Rice (2017) <doi:10.1093/aje/kwx225> for more details.

Efficiently impute large scale matrix with missing values via its unbiased low-rank matrix approximation. Our main approach is Hard-Impute algorithm proposed in <https://www.jmlr.org/papers/v11/mazumder10a.html>, which achieves highly computational advantage by truncated singular-value decomposition.

Converts large Danish register files ('sas7bdat') into Parquet format with year-based Hive partitioning and chunked reading for larger-than-memory files. Supports parallel conversion with a targets pipeline and reading those registers into DuckDB tables for faster querying and analyses.

Group Bayesian Networks: This package implements the inference of group Bayesian networks based on hierarchical feature clustering, and the adaptive refinement of the grouping regarding an outcome of interest, as described in Becker et. al (2021) <doi: 10.1371/journal.pcbi.1008735>.

This package provides statistical power analysis and sample size calculations for t-tests, ANOVA, regression, chi-square, proportion, correlation, nonparametric, biomarker, and clinical trial designs. Includes a scriptable API via power_compute(), publication-ready ggplot2 visualizations, and an optional Shiny application.

Simulating, visualizing and comparing tumor clonal data by using simple commands. This aims at providing a tool to help researchers to easily simulate tumor data and analyze the results of their approaches for studying the composition and the evolutionary history of tumors.

This package implements the vine copula based kernel density estimator of Nagler and Czado (2016) <doi:10.1016/j.jmva.2016.07.003>. The estimator does not suffer from the curse of dimensionality and is therefore well suited for high-dimensional applications.

This package performs extreme value analysis at multiple locations using functions from the evd package. Supports both point-based and gridded input data using the terra package, enabling flexible looping across spatial datasets for batch processing of generalised extreme value, Gumbel fits.

Given a CSV file with titles and abstracts, the package creates a document-term matrix that is lemmatized and stemmed and can directly be used to train machine learning methods for automatic title-abstract screening in the preparation of a meta analysis.

Compute important quantities when we consider stochastic systems that are observed continuously. Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press.