This package provides various R programming tools for plotting data, including:

• calculating and plotting locally smoothed summary function

• enhanced versions of standard plots

• manipulating colors

• calculating and plotting two-dimensional data summaries

• enhanced regression diagnostic plots

• formula-enabled interface to stats::lowess function

• displaying textual data in plots

• balloon plots

• plotting "Venn" diagrams

• displaying Open-Office style plots

• plotting multiple data on same region, with separate axes

• plotting means and confidence intervals

• spacing points in an x-y plot so they don't overlap

The googleVis package provides an interface between R and the Google Charts API. Google Charts offer interactive charts which can be embedded into web pages. The functions of the googleVis package allow the user to visualise data stored in R data frames with Google Charts without uploading the data to Google. The output of a googleVis function is HTML code that contains the data and references to JavaScript functions hosted by Google. googleVis makes use of the internal R HTTP server to display the output locally.

This tool supports analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. In addition, this tool takes care of calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format.

This package provides an implementation of dimensionality reduction via regression using Kernel Ridge Regression.

The extrafont package makes it easier to use fonts other than the basic PostScript fonts that R uses. Fonts that are imported into extrafont can be used with PDF or PostScript output files. There are two hurdles for using fonts in PDF (or Postscript) output files:

1. Making R aware of the font and the dimensions of the characters.

2. Embedding the fonts in the PDF file so that the PDF can be displayed properly on a device that doesn't have the font. This is usually needed if you want to print the PDF file or share it with others.

The extrafont package makes both of these things easier.

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 is an extension to the testthat package that makes it easy to add graphical unit tests. It provides a Shiny application to manage the test cases.

This package provides a replication of key functionality from dplyr and the wider tidyverse using only base.

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 performs score test using saddlepoint approximation to estimate the null distribution. It also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results.

This package contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e., the Kooperberg and Stone (1992) algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997).

This package provides an easy way to determine which directories on the user's computer should be used to save data, caches and logs. It is a port of Python's Appdirs to R.

This package serves two purposes:

1. Provide a comfortable R interface to query the Google server for static maps, and

2. Use the map as a background image to overlay plots within R. This requires proper coordinate scaling.

This package implements faster versions of base R functions (e.g. mean, standard deviation, covariance, weighted mean), mostly written in C++, along with miscellaneous functions for various purposes (e.g. create the histogram with fitted probability density function or probability mass function curve, create the body mass index groups, assess the linearity assumption in logistic regression).

This package provides syntax highlighting for R source code. Currently it supports LaTeX and HTML output. Source code of other languages is supported via Andre Simon's highlight package.

This package provides a general toolkit for downloading, managing, analyzing, and presenting data from the U.S. Census, including SF1 (Decennial short-form), SF3 (Decennial long-form), and the American Community Survey (ACS). Confidence intervals provided with ACS data are converted to standard errors to be bundled with estimates in complex acs objects. The package provides new methods to conduct standard operations on acs objects and present/plot data in statistically appropriate ways.

This package provides functionality to dynamically define R functions and S4 methods with inlined C, C++ or Fortran code supporting .C and .Call calling conventions.

This package contains the Rook specification and convenience software for building and running Rook applications. A Rook application is an R reference class object that implements a call method or an R closure that takes exactly one argument, an environment, and returns a list with three named elements: the status, the headers, and the body.

This package provides an alternative to R's built-in functionality for handling regular expressions, based on the Onigmo library. It offers first-class compiled regex objects, partial matching and function-based substitutions, amongst other features.

This package provides functionalities to build and manipulate probability distributions of the skew-normal family and some related ones, notably the skew-t family, and provides related statistical methods for data fitting and diagnostics, in the univariate and the multivariate case.

This package implements the following methods for event history analysis: Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits.

This package provides a matrix-like data structure that allows for efficient, convenient, and scalable subsetting of binary genotype/phenotype files generated by PLINK, the whole genome association analysis toolset, without loading the entire file into memory.

This package provides a replacement for the extract function from the raster package that is suitable for extracting raster values using sf polygons.

Intense parallel workloads can be difficult to monitor. Packages crew.cluster, clustermq, and future.batchtools distribute hundreds of worker processes over multiple computers. If a worker process exhausts its available memory, it may terminate silently, leaving the underlying problem difficult to detect or troubleshoot. Using the autometric package, a worker can proactively monitor itself in a detached background thread. The worker process itself runs normally, and the thread writes to a log every few seconds. If the worker terminates unexpectedly, autometric can read and visualize the log file to reveal potential resource-related reasons for the crash. The autometric package borrows heavily from the methods of packages ps and psutil.