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

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 package provides an R Markdown format for converting an R Markdown document to a grid-oriented dashboard. The dashboard flexibly adapts the size of its components to the containing web page.

This package lets you generate planar and spherical triangle meshes, compute finite element calculations for 1- and 2-dimensional flat and curved manifolds with associated basis function spaces, methods for lines and polygons, and transparent handling of coordinate reference systems and coordinate transformation, including sf and sp geometries. The core fmesher library code was originally part of the INLA package, and implements parts of "Triangulations and Applications" by Hjelle and Daehlen (2006) <doi:10.1007/3-540-33261-8>.

This package is a collection of miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. The data transformation functions also support labelled data, and all integrate seamlessly into a tidyverse workflow.

This package is an implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. It includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

This package lets you build complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as LeftOf, RightOf, TopOf, and Beneath and more. Other features include clustering, dendrograms and integration with ggplot2 generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.

This is an R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.

This package provides tools to create themes and color palettes for the package ggplot2.

This package provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.

This package provides a set of predicates and assertions for checking the properties of (country independent) complex data types. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package provides a set of predicates and assertions for checking the properties of files and connections. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.

Joyplots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in ggplot2.

Online data collection tools like Google Forms often export multiple-response questions with data concatenated in cells. The concat.split (cSplit) family of functions provided by this package splits such data into separate cells. This package also includes functions to stack groups of columns and to reshape wide data, even when the data are "unbalanced"---something which reshape (from base R) does not handle, and which melt and dcast from reshape2 do not easily handle.

Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). This package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided.

Query, set, and delete credentials from the git credential store. Manage GitHub tokens and other git credentials. This package is to be used by other packages that need to authenticate to GitHub and/or other git repositories.

This package provides a lightweight package to easily manipulate, clean, transform, and prepare your data for analysis. It also forms the data wrangling backend for the packages in the easystats ecosystem.

This is a data only package providing the algorithmic complexity of short strings, computed using the coding theorem method. For a given set of symbols in a string, all possible or a large number of random samples of Turing machines with a given number of states (e.g., 5) and number of symbols corresponding to the number of symbols in the strings were simulated until they reached a halting state or failed to end. This package contains data on 4.5 million strings from length 1 to 12 simulated on Turing machines with 2, 4, 5, 6, and 9 symbols. The complexity of the string corresponds to the distribution of the halting states.

This package provides medium to high level functions for 3D interactive graphics, including functions modelled on base graphics (plot3d(), etc.) as well as functions for constructing representations of geometric objects (cube3d(), etc.). Output may be on screen using OpenGL, or to various standard 3D file formats including WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.

This package gives you the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions.

User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the StanHeaders package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.

This package provides a package for quantifying, profiling and removing cell free mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments.

This package computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g. soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method.