This package extends the ggplot2 plotting system to support network visualization. Inspired by ggtree, ggtangle is designed to work with network associated data.

This package enables you to estimate the p-values for predictors x against target variable y in Lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic.

This tool generates high number of both single- and multi-objective test functions. These functions are frequently used for the benchmarking of (numerical) optimization algorithms. Moreover, it offers a set of convenient functions to generate, plot and work with objective functions.

This package provides extensions to ggplot2, respecting the grammar of its graphics paradigm.

This package provides an R interface to the NCBI's EUtils API, allowing users to search databases like GenBank PubMed, process the results of those searches and pull data into their R sessions.

Format dates and times flexibly and to whichever locales make sense. This package parses dates, times, and date-times in various formats (including string-based ISO 8601 constructions). The formatting syntax gives the user many options for formatting the date and time output in a precise manner. Time zones in the input can be expressed in multiple ways and there are many options for formatting time zones in the output as well. Several of the provided helper functions allow for automatic generation of locale-aware formatting patterns based on date/time skeleton formats and standardized date/time formats with varying specificity.

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 function to format R source code. Spaces and indent will be added to the code automatically, and comments will be preserved under certain conditions, so that R code will be more human-readable and tidy. There is also a Shiny app as a user interface in this package.

This package provides utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models, this package implements features like standardization or bootstrapping of parameters and models, feature reduction (feature extraction and variable selection) as well as conversion between indices of effect size.

This package provides methods to create, store, access, and manipulate large matrices. Matrices are allocated to shared memory and may use memory-mapped files.

This package contains a function that imports data from a CSV file, or uses manually entered data from the format (x, y, weight) and plots the appropriate ACC vs LOI graph and LMA graph. The main function is plotLMA (source file, header) that takes a data set and plots the appropriate LMA and ACC graphs. If no source file (a string) was passed, a manual data entry window is opened. The header parameter indicates by TRUE/FALSE (false by default) if the source CSV file has a header row or not. The dataset should contain only one independent variable (x) and one dependent variable (y) and can contain a weight for each observation.

This package implements numerically-stable Gauss-Hermite quadrature rules and utility functions for adaptive GH quadrature.

This package provides an implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available.

This package provides a set of tools to perform Quantitative Trait Locus (QTL) analysis in experimental crosses. It is a reimplementation of the R/qtl package to better handle high-dimensional data and complex cross designs. Broman et al. (2018) <doi:10.1534/genetics.118.301595>.

This package provides a Database Interface (DBI) compliant driver for R to access PostgreSQL database systems.

This package provides a cross-platform Zip compression library for R. It is a replacement for the zip function, that does not require any additional external tools on any platform.

This package provides tools to create Class Cover Catch Digraphs, neighborhood graphs, and relatives.

This package provides tools and functions for managing the download of binary files. Binary repositories are defined in the YAML format. Defining new pre-download, download and post-download templates allow additional repositories to be added.

The vegan package provides tools for descriptive community ecology. It has most basic functions of diversity analysis, community ordination and dissimilarity analysis. Most of its multivariate tools can be used for other data types as well.

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 gdtools package provides functionalities to get font metrics and to generate base64 encoded string from raster matrix.

Tidygeocoder makes getting data from geocoding services easy. A unified high-level interface is provided for a selection of supported geocoding services and results are returned in tibble format.

Recipes is an extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models.

Convert a logical vector or a vector of p-values or a correlation, difference, or distance matrix into a display identifying the pairs for which the differences were not significantly different.