This package provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). This package supports RDF by implementing an R interface to the Redland RDF C library. In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes.

This package provides functions and an RStudio add-in that search a BibTeX or BibLaTeX file to create and insert formatted Markdown citations into the current document.

This package provides tools for the computation of the matrix exponential, logarithm, square root, and related quantities.

This package enables conversions between R objects and JavaScript Object Notation (JSON) using the rapidjsonr library.

This package provides text and label geometries for ggplot2 that help to avoid overlapping text labels. Labels repel away from each other and away from the data points.

This package simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of Rcpp, read/write times are comparable to the xlsx and XLConnect packages with the added benefit of removing the dependency on Java.

XML-RPC is a remote procedure call protocol based on XML. The xmlrpc2 package is inspired by the XMLRPC package but uses the curl and xml2 packages instead of RCurl and XML'.

This package provides qualitatively constrained (regression) smoothing splines via linear programming and sparse matrices.

This package provides functions for Constraint Based Simulation using Flux Balance Analysis and informative analysis of the data generated during simulation.

This package provides implementations of the SHA-3 cryptographic hash and SHAKE256 extendable-output functions (XOF).

This package provides a toolkit of functions for nonlinear regression and repeated measurements. It was designated to be imported by other packages such as gnlm, stable, growth, repeated, and event.

dplyr is the next iteration of plyr. It is focused on tools for working with data frames. It has three main goals: 1) identify the most important data manipulation tools needed for data analysis and make them easy to use in R; 2) provide fast performance for in-memory data by writing key pieces of code in C++; 3) use the same code interface to work with data no matter where it is stored, whether in a data frame, a data table or database.

This is a C/C++ based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R. It further includes fast functions for common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data.

This package provides a suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.

This package provides a fast, flexible, and comprehensive framework for quantitative text analysis in R. It provides functionality for corpus management, creating and manipulating tokens and ngrams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.

This is a package for estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, kernel properties, quantiles and integration.

This package provides tools for creating detailed dataframes for common statistical approaches and tests. These include parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for ggstatsplot.

This package provides a pipeline toolkit for statistics and data science in R; the targets package brings function-oriented programming to Make-like declarative pipelines. It orchestrates a pipeline as a graph of dependencies, skips steps that are already up to date, runs the necessary computation with optional parallel workers, abstracts files as R objects, and provides tangible evidence that the results are reproducible given the underlying code and data. The methodology in this package borrows from GNU Make (2015, ISBN:978-9881443519) and drake (2018, <doi:10.21105/joss.00550>).

This package provides R bindings to OpenSSL libssl and libcrypto, plus custom SSH pubkey parsers. It supports RSA, DSA and NIST curves P-256, P-384 and P-521. Cryptographic signatures can either be created and verified manually or via x509 certificates. AES block cipher is used in CBC mode for symmetric encryption; RSA for asymmetric (public key) encryption. High-level envelope functions combine RSA and AES for encrypting arbitrary sized data. Other utilities include key generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random number generator, and bignum math methods for manually performing crypto calculations on large multibyte integers.

This package provides basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).

Rasterize only specific layers of a ggplot2 plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without losing vector properties of the scale-sensitive information.

This package uses the node library is-my-json-valid or ajv to validate JSON against a JSON schema. Drafts 04, 06 and 07 of JSON schema are supported.

This package provides functions to perform reproducible parallel foreach loops, using independent random streams as generated by L'Ecuyer's combined multiple-recursive generator. It enables to easily convert standard %dopar% loops into fully reproducible loops, independently of the number of workers, the task scheduling strategy, or the chosen parallel environment and associated foreach backend.

This package allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model. Flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. Afpt can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions.