This package is a port of the new matplotlib color maps (viridis, magma, plasma and inferno) to R. matplotlib is a popular plotting library for Python. These color maps are designed in such a way that they will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. They are also designed to be perceived by readers with the most common form of color blindness. This is the lite version of the more complete viridis package.

This package enables variogram modelling, including: simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; and sequential Gaussian or indicator (co)simulation. It includes variogram and variogram map plotting utility functions, and supports sf and stars.

The CommonMark specification defines a rationalized version of markdown syntax. This package uses the cmark reference implementation for converting markdown text into various formats including HTML, LaTeX and groff man. In addition, it exposes the markdown parse tree in XML format. The latest version of this package also adds support for Github extensions including tables, autolinks and strikethrough text.

This package provides tools to create interactive chords diagrams via the D3 Javascript library. Chord diagrams show directed relationships among a group of entities. This package is based on http://bl.ocks.org/mbostock/4062006 with some modifications (fading) and additions (tooltips, bipartite diagram type).

This package supports multiple precision arithmetic (big integers and rationals, prime number tests, matrix computation), "arithmetic without limitations" using the GNU Multiple Precision library.

This package provides tools for the calibration of penalized criteria for model selection. The calibration methods available are based on the slope heuristics.

This package contains a collection of functions to deal with nonparametric measurement error problems using deconvolution kernel methods. We focus two measurement error models in the package: (1) an additive measurement error model, where the goal is to estimate the density or distribution function from contaminated data; (2) nonparametric regression model with errors-in-variables. The R functions allow the measurement errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the "Fast Fourier Transform" (FFT) algorithm for density estimation with error-free data to the deconvolution kernel estimation. Several methods for the selection of the data-driven smoothing parameter are also provided in the package. See details in: Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24.

This package provides .C64(), an enhanced version of .C() and .Fortran() from the R foreign function interface. .C64() supports long vectors, arguments of type 64-bit integer, and provides a mechanism to avoid unnecessary copies of read-only and write-only arguments. This makes it a convenient and fast interface to C/C++ and Fortran code.

The pscl is an R package providing classes and methods for:

1. Bayesian analysis of roll call data (item-response models);

2. elementary Bayesian statistics;

3. maximum likelihood estimation of zero-inflated and hurdle models for count data;

4. utility functions.

This package provides basic I/O tools for streaming and data parsing.

There are three main goals to the vctrs package:

1. To propose vec_size() and vec_type() as alternatives to length() and class(). These definitions are paired with a framework for type-coercion and size-recycling.

2. To define type- and size-stability as desirable function properties, use them to analyse existing base function, and to propose better alternatives. This work has been particularly motivated by thinking about the ideal properties of c(), ifelse(), and rbind().

3. To provide a new vctr base class that makes it easy to create new S3 vectors. vctrs provides methods for many base generics in terms of a few new vctrs generics, making implementation considerably simpler and more robust.

The smurf package contains the implementation of the Sparse Multi-type Regularized Feature (SMuRF) modeling algorithm to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood. Next to the fitting procedure, following functionality is available:

• Selection of the regularization tuning parameter lambda using three different approaches: in-sample, out-of-sample or using cross-validation.

• S3 methods to handle the fitted object including visualization of the coefficients and a model summary.

This package provides miscellaneous functions to help customize ggplot2 objects. High-level functions are provided to post-process ggplot2 layouts and allow alignment between plot panels, as well as setting panel sizes to fixed values. Other functions include a custom geom, and helper functions to enforce symmetric scales or add tags to facetted plots.

This package provides an R module for display of maps. Projection code and larger maps are in separate packages (mapproj and mapdata).

This package provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations).

With this package you can add in-app user authentication to Shiny, allowing you to secure publicly hosted apps and build dynamic user interfaces from user information.

The R package data.table is an extension of data.frame providing functions for fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group, column listing and fast file reading.

This package provides a syntax highlighter for R code based on the results of the R parser. It supports rendering in HTML and LaTeX markup. It includes a custom Sweave driver performing syntax highlighting of R code chunks.

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.

r-rvest helps you scrape information from web pages. It is designed to work with magrittr to make it easy to express common web scraping tasks, inspired by libraries like BeautifulSoup.

This package provides an integrated set of functions for the analysis of multivariate normal datasets with missing values, including implementation of the EM algorithm, data augmentation, and multiple imputation.

This is a package for maximum likelihood estimation of censored regression (Tobit) models with cross-sectional and panel data.

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