This package provides methods for cluster analysis. It is a much extended version of the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".

This package implements tools designed to collect and organize Twitter data via Twitter's REST and stream Application Program Interfaces (API).

This package provides helper functions that act as wrappers to more advanced statistical methods with the advantage of having sane defaults for quick reporting.

Postprocessors refine predictions outputted from machine learning models to improve predictive performance or better satisfy distributional limitations. This package introduces tailor objects, which compose iterative adjustments to model predictions. A number of pre-written adjustments are provided with the package, such as calibration. See Lichtenstein, Fischhoff, and Phillips (1977) <doi:10.1007/978-94-010-1276-8_19>. Other methods and utilities to compose new adjustments are also included. Tailors are tightly integrated with the tidymodels framework.

This package provides tools to identify global ("unknown" or "free") objects in R expressions by code inspection using various strategies, e.g. conservative or liberal. The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in distributed compute environments.

This package provides tools for making the descriptive "Table 1" used in medical articles, a transition plot for showing changes between categories (also known as a Sankey diagram), flow charts by extending the grid package, a method for variable selection based on the SVD, Bezier lines with arrows complementing the ones in the grid package, and more.

This package provides tools to read, write, create, and manipulate DESCRIPTION files. It is intended for packages that create or manipulate other packages.

This package provides tools for functional enrichment analysis, gene identifier conversion and mapping homologous genes across related organisms via the g:Profiler toolkit.

This package can be used to solve Linear Programming / Linear Optimization problems by using the simplex algorithm.

This package provides enhanced message functions (cat() / message() / warning() / error()) using wrappers around sprintf(). It also provides multiple assertion functions (e.g. to check class, length, values, files, arguments, etc.).

This package provides functions to manipulate binary fingerprints of arbitrary length. A fingerprint is represented by an object of S4 class fingerprint. The bitwise logical functions in R are overridden so that they can be used directly with fingerprint objects. A number of distance metrics are also available. Fingerprints can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and can also be folded. Arbitrary fingerprint formats can be handled via line handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.

The C++ header files of the Stan project are provided by this package. There is a shared object containing part of the CVODES library, but it is not accessible from R. r-stanheaders is only useful for developers who want to utilize the LinkingTo directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies.

The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or variational methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, templated statistical and linear algebra functions that can handle the automatically differentiable scalar types (and doubles, ints, etc.), and a parser for the Stan language. The r-rstan package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.

Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation.

The main function archetypes implements a framework for archetypal analysis supporting arbitrary problem solving mechanisms for the different conceptual parts of the algorithm.

This package provides an interface to Amazon Web Services database services, including Relational Database Service (RDS), DynamoDB NoSQL database, and more.

This package provides a utility for R to parse a bibtex file.

This package provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class family, link-glm, lm, glm and betareg. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the enriched_glm function that has the same interface as glm but results in objects of class enriched_glm. In addition to the usual components in a glm object, enriched_glm objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The NNG-powered mirai R package by Gao (2023) <doi:10.5281/zenodo.7912722> is a scheduler that efficiently processes these intense workloads. The crew package extends mirai with a unifying interface for third-party worker launchers.

The Rmisc library contains functions for data analysis and utility operations.

This package provides the cumulative distribution function (CDF), quantile, and statistical power calculator for a collection of thresholding Fisher's p-value combination methods, including Fisher's p-value combination method, truncated product method and, in particular, soft-thresholding Fisher's p-value combination method which is proven to be optimal in some context of signal detection. The p-value calculator for the omnibus version of these tests are also included.

The feature package contains functions to display and compute kernel density estimates, significant gradient and significant curvature regions. Significant gradient and/or curvature regions often correspond to significant features (e.g. local modes).

colorout is an R package that colorizes R output when running in terminal emulator.

R STDOUT is parsed and numbers, negative numbers, dates in the standard format, strings, and R constants are identified and wrapped by special ANSI scape codes that are interpreted by terminal emulators as commands to colorize the output. R STDERR is also parsed to identify the expressions warning and error and their translations to many languages. If these expressions are found, the output is colorized accordingly; otherwise, it is colorized as STDERROR (blue, by default).

You can customize the colors according to your taste, guided by the color table made by the command show256Colors(). You can also set the colors to any arbitrary string. In this case, it is up to you to set valid values.

This package provides utilities for computing measures to assess model quality, which are not directly provided by R's base or stats packages. These include e.g. measures like r-squared, intraclass correlation coefficient, root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models.

This package parses HTTP request data in application/json, multipart/form-data, or application/x-www-form-urlencoded format. It includes an example of hosting and parsing HTML form data in R using either httpuv or Rhttpd.