This package provides support for rendering of formatted text using Grid graphics. Text can be formatted via a minimal subset of Markdown, HTML, and inline CSS directives, and it can be rendered both with and without word wrap.

This package provides an implementation of interpreted string literals, inspired by Python's Literal String Interpolation (PEP-0498) and Docstrings (PEP-0257) and Julia's Triple-Quoted String Literals.

R's default conflict management system gives the most recently loaded package precedence. This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. The conflicted package takes a different approach, making every conflict an error and forcing you to choose which function to use.

This package calls the Jupyter script nbconvert to create vignettes from notebooks. Those notebooks (.ipynb files) are files containing rich text, code, and its output. Code cells can be edited and evaluated interactively.

This package provides a self-tuning spectral clustering method for single or multi-view data. Spectrum uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. Spectrum uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.

The grammar of graphics as implemented in ggplot2 is a poor fit for graph and network visualizations due to its reliance on tabular data input. The ggraph package is an extension of the ggplot2 API tailored to graph visualizations and provides the same flexible approach to building up plots layer by layer.

This package provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses. The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the brglm2 package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration.

This package provides a toolkit for working with Biological Observation Matrix (BIOM) files. Features include reading/writing all BIOM formats, rarefaction, alpha diversity, beta diversity (including UniFrac), summarizing counts by taxonomic level, and sample subsetting. Standalone functions for reading, writing, and subsetting phylogenetic trees are also provided.

This package contains various routines for drawing ellipses and ellipse-like confidence regions, implementing the plots described in Murdoch and Chow (1996), A graphical display of large correlation matrices, The American Statistician 50, 178-180. There are also routines implementing the profile plots described in Bates and Watts (1988), Nonlinear Regression Analysis and its Applications.

This package provides an R interface to Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen and Guestrin (2016). The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.

This package provides optimized functions and flexible combinatorial iterators implemented in C++ for solving problems in combinatorics and computational mathematics. It utilizes the RMatrix class from RcppParallel for thread safety. There are combination/permutation functions with constraint parameters that allow for generation of all results of a vector meeting specific criteria. It is capable of generating specific combinations/permutations which sets up nicely for parallelization as well as random sampling. Gmp support permits exploration where the total number of results is large. Additionally, there are several high performance number theoretic functions that are useful for problems common in computational mathematics.

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the INLA package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

This package provides two convenience functions assert() and test_pkg() to facilitate testing R packages.

This package provides software to accompany the book "Wavelet Methods for Time Series Analysis", Donald B. Percival and Andrew T. Walden, Cambridge University Press, 2000.

Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for analysis and visualization.

This package contains linear and nonlinear regression methods based on partial least squares and penalization techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing.

This package provides a set of functions with example data for graphing, pruning, and mapping models. These models are from hierarchical clustering, and classification and regression trees.

This package provides tools for testing, monitoring and dating structural changes in (linear) regression models. It features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data.

This package wraps the AntiWord utility to extract text from Microsoft Word documents. The utility only supports the old doc format, not the new XML based docx format. Use the xml2 package to read the latter.

This package provides a comprehensive collection for structural multivariate function estimation using smoothing splines.

This package provides a scripting and command-line front-end is provided by r (aka littler) as a lightweight binary wrapper around the GNU R language and environment for statistical computing and graphics. While R can be used in batch mode, the r binary adds full support for both shebang-style scripting (i.e. using a hash-mark-exclamation-path expression as the first line in scripts) as well as command-line use in standard pipelines. In other words, r provides the R language without the environment.

This is a package to read raw accelerometry from GT3X+ accelerometry data and plain table data to calculate the Activity Index from Bai et al. (2016) doi:10.1371/journal.pone.0160644.

This package provides tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). The area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

This package provides tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.