This package provides a collection of R functions to perform nonparametric analysis of covariance for regression curves or surfaces. Testing the equality or parallelism of nonparametric curves or surfaces is equivalent to analysis of variance (ANOVA) or analysis of covariance (ANCOVA) for one-sample functional data. Three different testing methods are available in the package, including one based on L-2 distance, one based on an ANOVA statistic, and one based on variance estimators.

This package provides an R interface to Google's BigQuery database.

The main function biclust() provides several algorithms to find biclusters in two-dimensional data, spectral, plaid model, xmotifs, and bimax. In addition, the package provides methods for data preprocessing (normalization and discretization), visualization, and validation of bicluster solutions.

The C++ header files of the Stan project are provided by this package. There is a shared object containing part of the CVODES library, but it is not accessible from R. r-stanheaders is only useful for developers who want to utilize the LinkingTo directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies.

The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or variational methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, templated statistical and linear algebra functions that can handle the automatically differentiable scalar types (and doubles, ints, etc.), and a parser for the Stan language. The r-rstan package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.

This package provides some easy-to-use functions to extract and visualize the output of multivariate data analyses, including PCA (Principal Component Analysis), CA (Correspondence Analysis), MCA (Multiple Correspondence Analysis), FAMD (Factor Analysis of Mixed Data), MFA (Multiple Factor Analysis) and HMFA (Hierarchical Multiple Factor Analysis) functions from different R packages. It contains also functions for simplifying some clustering analysis steps and provides ggplot2-based elegant data visualization.

This package provides functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. It boasts support for many additional probability distributions to model insurance loss amounts and loss frequency: 19 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. It also supports phase-type distributions commonly used to compute ruin probabilities.

This package provides a set of restricted permutation designs for freely exchangeable, line transects (time series), spatial grid designs and permutation of blocks (groups of samples). permute also allows split-plot designs, in which the whole-plots or split-plots or both can be freely exchangeable.

This package provides exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis.

This package provides an R port of the library Clipper. It performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. It computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. It computes the Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.

This package provides Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of RcppArmadillo to speed up the computationally intensive parts of the functions. For more information, see

1. "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, https://doi.org/10.18637/jss.v001.i04;

2. "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, https://doi.org/10.1145/1772690.1772862;

3. "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, https://doi.org/10.21105/joss.00026;

4. "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, https://doi.org/10.1126/science.1136800.

This package implements multitaper spectral estimation techniques using prolate spheroidal sequences (Slepians) and sine tapers for time series analysis. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.

This package provides a new class Formula, which extends the base class formula. It supports extended formulas with multiple parts of regressors on the right-hand side and/or multiple responses on the left-hand side.

Extracts sentiment and sentiment-derived plot arcs from text using a variety of sentiment dictionaries conveniently packaged for consumption by R users. Implemented dictionaries include syuzhet (default) developed in the Nebraska Literary Lab, afinn developed by Finn Arup Nielsen, bing developed by Minqing Hu and Bing Liu, and nrc developed by Mohammad, Saif M. and Turney, Peter D. Applicable references are available in README.md and in the documentation for the get_sentiment function. The package also provides a hack for implementing Stanford's coreNLP sentiment parser. The package provides several methods for plot arc normalization.

RestRserve is an R web API framework for building high-performance AND robust microservices and app backends. With Rserve backend on UNIX-like systems it is parallel by design. It will handle incoming requests in parallel - each request in a separate fork.

This is a package for ratios of count data such as obtained from RNA-seq are modelled using Bayesian statistics to derive posteriors for effects sizes. This approach is described in Erhard & Zimmer (2015) <doi:10.1093/nar/gkv696> and Erhard (2018) <doi:10.1093/bioinformatics/bty471>.

This package provides an interface to Amazon Web Services compute services, including Elastic Compute Cloud (EC2), Lambda functions-as-a-service, containers, batch processing, and more.

This package provides tools to compute Gower's distance (or similarity) coefficient between records, and to compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP.

This package provides a general purpose toolbox for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Functions for simulating and testing particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics.

Logging functions in RcppSpdlog provide access to the logging functionality from the spdlog C++ library. This package offers shorter convenience wrappers for the R functions which match the C++ functions, namely via, say, spdl::debug() at the debug level. The actual formatting is done by the fmt::format() function from the fmtlib library (that is also std::format() in C++20 or later).

This package provides tools for handling Base64 encoding. It is more flexible than the orphaned "base64" package.

This package implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: linkage() in the SciPy package scipy.cluster.hierarchy, hclust() in R's stats package, and the flashClust package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide.

The aim of httr is to provide a wrapper for RCurl customised to the demands of modern web APIs. It provides useful tools for working with HTTP organised by HTTP verbs (GET(), POST(), etc). Configuration functions make it easy to control additional request components.

This package exposes R bindings to jsTree, a JavaScript library that supports interactive trees, to enable rich, editable trees in Shiny.

The ggplot2 package is an excellent and flexible package for elegant data visualization in R. However the default generated plots require some formatting before we can send them for publication. The ggpubr package provides some easy-to-use functions for creating and customizing ggplot2-based publication-ready plots.