The mlr3 package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core mlr3 packages.

This package provides a fast implementation of hierarchical clustering.

This package provides sparse vectors powered by ALTREP (Alternative Representations for R Objects) that behave like regular vectors, and can thus be used in data frames. It also provides tools to convert between sparse matrices and data frames with sparse columns and functions to interact with sparse vectors.

This package provides simulation methods for the evolution of antibody repertoires. The heavy and light chain variable region of both human and C57BL/6 mice can be simulated in a time-dependent fashion. Both single lineages using one set of V-, D-, and J-genes or full repertoires can be simulated. The algorithm begins with an initial V-D-J recombination event, starting the first phylogenetic tree. Upon completion, the main loop of the algorithm begins, with each iteration representing one simulated time step. Various mutation events are possible at each time step, contributing to a diverse final repertoire.

This package provides an interface to Amazon Web Services compute services, including Elastic Compute Cloud (EC2), Lambda functions-as-a-service, containers, batch processing, and more.

This package provides linear models based on Theil-Sen single median and Siegel repeated medians. They are very robust (29 or 50 percent breakdown point, respectively), and if no outliers are present, the estimators are very similar to OLS.

This package extends the out of memory vectors of ff with statistical functions and other utilities to ease their usage.

Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known.

The TOML configuration format specifies an excellent format suitable for both human editing as well as the common uses of a machine-readable format. This package provides Rcpp bindings to a TOML parser.

This package provides authentication helpers for Snowflake. It provides compatibility with authentication approaches supported by the Snowflake Connector for Python and the Snowflake CLI.

This package provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling.

This package provides methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields.

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.

UpSet plots are an improvement over Venn Diagram for set overlap visualizations. Striving to bring the best of the UpSetR and ggplot2, this package offers a way to create complex overlap visualisations, using simple and familiar tools.

This package provides an R interface to the dygraphs JavaScript charting library (a copy of which is included in the package). It provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting.

This package provides several analysis-related functions for the book entitled "R statistics and graph for medical articles" (written in Korean), version 1, by Keon-Woong Moon with Korean demographic data with several plot functions.

This package is a collection of tools to load R packages and automatically generate BibTeX files citing them as well as load and cache plain-text and Excel formatted data stored on GitHub, and from other sources.

This package provides a collection of tools to make working with physical measurements easier. One can convert between metric and imperial units, or calculate a dimension's unknown value from other dimensions' measurements.

This r-rbenchmark package is inspired by the Perl module Benchmark, and is intended to facilitate benchmarking of arbitrary R code. The library consists of just one function, benchmark, which is a simple wrapper around system.time. Given a specification of the benchmarking process (counts of replications, evaluation environment) and an arbitrary number of expressions, benchmark evaluates each of the expressions in the specified environment, replicating the evaluation as many times as specified, and returning the results conveniently wrapped into a data frame.

This package extends several functions to the complex domain, including the matrix exponential and logarithm, and the determinant.

Manipulate and visualize colors in a intuitive, low-dependency and functional way.

LIGER is a package for integrating and analyzing multiple single-cell datasets, developed and maintained by the Macosko lab. It relies on integrative non-negative matrix factorization to identify shared and dataset-specific factors.

This package provides tools for Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).

Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.