This package implements general purpose tools, such as functions for sampling and basic manipulation of Brazilian lawsuits identification number. It also implements functions for text cleaning, such as accentuation removal.

This package implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done.

This package provides helper functions with a consistent interface to coerce and verify the types and shapes of values for input checking.

This package provides functions for robust principal component analysis (PCA) by projection pursuit.

In order to create smooth animation between states of data, tweening is necessary. This package provides a range of functions for creating tweened data that can be used as basis for animation. Furthermore it adds a number of vectorized interpolaters for common R data types such as numeric, date and color.

This package provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to:

1. Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions.

2. Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws.

3. Provide various summaries of draws in convenient formats.

4. Provide lightweight implementations of state of the art posterior inference diagnostics.

This package provides functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>. Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2.

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

The R package ggplot2 is a plotting system based on the grammar of graphics. GGally extends ggplot2 by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.

This is a package to simplify loading of system fonts and Google Fonts into R, in order to support other packages.

This is an R package for spell checking common document formats including LaTeX, markdown, manual pages, and DESCRIPTION files. It includes utilities to automate checking of documentation and vignettes as a unit test during R CMD check. Both British and American English are supported out of the box and other languages can be added. In addition, packages may define a wordlist to allow custom terminology without having to abuse punctuation.

This package provides interactive visualizations for profiling R code.

This package allows you to render vector-based SVG images into high-quality custom-size bitmap arrays using the librsvg2 library. The resulting bitmap can be written to e.g. PNG, JPEG or WEBP format. In addition, the package can convert images directly to various formats such as PDF or PostScript.

Geometry shapes in R are typically represented by matrices (points, lines), with more complex shapes being lists of matrices (polygons). Geometries will convert various R objects into these shapes. Conversion functions are available at both the R level, and through Rcpp.

This package provides tools for HTML generation and output in R.

This package provides routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons.

This package creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().

This package provides some low-level utilities to use for R package development. It currently provides managers for multiple package specific options and registries, vignette, unit test and bibtex related utilities.

This package provides a comprehensive library for date-time manipulations using a new family of orthogonal date-time classes (durations, time points, zoned-times, and calendars) that partition responsibilities so that the complexities of time zones are only considered when they are really needed. Capabilities include: date-time parsing, formatting, arithmetic, extraction and updating of components, and rounding.

This package provides a pillar generic designed for formatting columns of data using the full range of colours provided by modern terminals.

This package provides functions for applying a wide range of fisheries stock assessment methods.

This package contains an implementation of a function digest() for the creation of hash digests of arbitrary R objects (using the md5, sha-1, sha-256, crc32, xxhash and murmurhash algorithms) permitting easy comparison of R language objects, as well as a function hmac() to create hash-based message authentication code.

Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as OpenSSL should be used.

This package provides high level functions for parallel programming with Rcpp. For example, the parallelFor() function can be used to convert the work of a standard serial for loop into a parallel one and the parallelReduce() function can be used for accumulating aggregates or other values.

This package provides a platform-independent API to access the operating system's credential store. It currently supports Keychain on macOS, Credential Store on Windows, the Secret Service API on GNU/Linux, and a simple, platform independent store implemented with environment variables. Additional storage back-ends can be added easily.