This package provides a framework for text mining applications within R.

This package provides an R wrapper to the Python natural language processing (NLP) library spaCy, from http://spacy.io.

This package is an implementation of about 6 major classes of statistical regression models. Currently only fixed-effects models are implemented, i.e., no random-effects models. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE, using Fisher scoring. VGLMs can be loosely thought of as multivariate generalised linear models.

This package provides fast and accurate convolution-type smoothed quantile regression, implemented using Barzilai-Borwein gradient descent with a Huber regression warm start. Confidence intervals for regression coefficients are constructed using multiplier bootstrap.

This package fits generalized linear models efficiently using RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner to help safeguard against convergence issues.

This package provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). This package supports RDF by implementing an R interface to the Redland RDF C library. In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes.

This package creates a lightweight way to add markdown helpfiles to Shiny apps, using modal dialog boxes, with no need to observe each help button separately.

This package provides zm, a utility that allows you to zoom/navigate any plot when called with any active plot.

Full 64-bit resolution date and time functionality with nanosecond granularity is provided, with easy transition to and from the standard POSIXct type. Three additional classes offer interval, period and duration functionality for nanosecond-resolution timestamps.

This package provides an arsenal of R functions for large-scale statistical summaries, which are streamlined to work within the latest reporting tools in R and RStudio and which use formulas and versatile summary statistics for summary tables and models. The primary functions include

1. tableby, a Table-1-like summary of multiple variable types by the levels of one or more categorical variables;

2. paired, a Table-1-like summary of multiple variable types paired across two time points;

3. modelsum, which performs simple model fits on one or more endpoints for many variables (univariate or adjusted for covariates);

4. freqlist, a powerful frequency table across many categorical variables;

5. comparedf, a function for comparing data.frames; and

6. write2, a function to output tables to a document.

Sankey plots are a type of diagram that is convenient to illustrate how flow of information, resources etc. separates and joins, much like observing how rivers split and merge. For example, they can be used to compare different clusterings. This package provides an implementation of Sankey plots for R.

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

This package provides datasets to accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage.

This method identifies topological domains in genomes from Hi-C sequence data. The authors published an implementation of their method as an R script. This package originates from those original TopDom R scripts and provides help pages adopted from the original TopDom PDF documentation. It also provides a small number of bug fixes to the original code.

This package contains the datasets and a few functions for use with the practicals outlined in Appendix A of the book Statistical Models (Davison, 2003, Cambridge University Press). The practicals themselves can be found at http://statwww.epfl.ch/davison/SM/.

This package provides a suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression.

This package is a collection of several algorithms to obtain archetypoids with small and large databases and with both classical multivariate data and functional data (univariate and multivariate). Some of these algorithms also detect anomalies (outliers).

This package provides an implementation of heatmaps that offers more control over dimensions and appearance.

In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics that are not provided by base R methods related to model fitting.

Solving a system of linear equations is one of the most fundamental computational problems for many fields of mathematical studies, such as regression problems from statistics or numerical partial differential equations. This package provides basic stationary iterative solvers such as Jacobi, Gauss-Seidel, Successive Over-Relaxation and SSOR methods. Nonstationary, also known as Krylov subspace methods are also provided. Sparse matrix computation is also supported in that solving large and sparse linear systems can be manageable using the Matrix package along with RcppArmadillo.

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.

Obtain any major version of jQuery and use it in any webpage generated by htmltools (e.g. shiny, htmlwidgets, and rmarkdown). Most R users don't need to use this package directly, but other R packages (e.g. shiny, rmarkdown, etc.) depend on this package to avoid bundling redundant copies of jQuery.

This is a package to infer transmission trees from a dated phylogeny. It includes methods to simulate and analyze outbreaks. The methodology is described in Didelot et al. (2014) and Didelot et al. (2017).

This package provides other packages with access to the internal R serialization code. Access to this code is provided at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package.