This package provides bindings to ImageMagick, a comprehensive image processing library. It supports many common formats (PNG, JPEG, TIFF, PDF, etc.) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio, images are automatically previewed when printed to the console, resulting in an interactive editing environment.

Phylogenetic trees generally contain multiple components including nodes, edges, branches and associated data. This package provides an approach to convert tree objects to tidy data frames. It also provides tidy interfaces to manipulate tree data.

This package provides useful functions to edit ggplot object (e.g., setting fonts for theme and layers, adding rounded rectangle as background for each of the legends).

This package represents an implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization.

This package provides an interface to Amazon Web Services customer engagement services, including Simple Email Service, Connect contact center service, and more.

This package provides an R implementation of the Octave package signal, containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

Stringr is a consistent, simple and easy to use set of wrappers around the fantastic stringi package. All function and argument names (and positions) are consistent, all functions deal with "NA"'s and zero length vectors in the same way, and the output from one function is easy to feed into the input of another.

Rserve acts as a socket server (TCP/IP or local sockets) which allows binary requests to be sent to R. Every connection has a separate workspace and working directory. Client-side implementations are available for popular languages such as C/C++ and Java, allowing any application to use facilities of R without the need of linking to R code. Rserve supports remote connection, user authentication and file transfer. A simple R client is included in this package as well.

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

This r-rbenchmark package is inspired by the Perl module Benchmark, and is intended to facilitate benchmarking of arbitrary R code. The library consists of just one function, benchmark, which is a simple wrapper around system.time. Given a specification of the benchmarking process (counts of replications, evaluation environment) and an arbitrary number of expressions, benchmark evaluates each of the expressions in the specified environment, replicating the evaluation as many times as specified, and returning the results conveniently wrapped into a data frame.

This package provides an estimation and inference methods for models of conditional quantiles: linear and nonlinear parametric and non-parametric models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also included.

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 provides a cross between a 2D density plot and a scatter plot, implemented as a ggplot2 geom. Points in the scatter plot are colored by the number of neighboring points. This is useful to visualize the 2D-distribution of points in case of overplotting.

This package provides various tools for creating iterators, many patterned after functions in the Python itertools module, and others patterned after functions in the snow package.

This package is an R package designed for QC, analysis, and exploration of single cell RNA-seq data. It easily enables widely-used analytical techniques, including the identification of highly variable genes, dimensionality reduction; PCA, ICA, t-SNE, standard unsupervised clustering algorithms; density clustering, hierarchical clustering, k-means, and the discovery of differentially expressed genes and markers.

This package provides utilities for computing measures to assess model quality, which are not directly provided by R's base or stats packages. These include e.g. measures like r-squared, intraclass correlation coefficient, root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models.

This package includes functions for processing GeoJson objects relying on RFC 7946. The geojson encoding is based on json11, a tiny JSON library for C++11. Furthermore, the source code is exported in R through the Rcpp and RcppArmadillo packages.

This package provides a Wrapper around the SVDLIBC library for (truncated) singular value decomposition of a sparse matrix. Currently, only sparse real matrices in Matrix package format are supported.

This package provides a fully DBI-compliant Rcpp-backed interface to PostgreSQL, a relational database.

This package enables conversions between R objects and JavaScript Object Notation (JSON) using the rapidjsonr library.

This package analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).

This package provides easy-to-use and versatile functions to output R objects in HTML format.

This package provides a suite of methods for powerful and robust microbiome data analysis, including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances, Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature- based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA:

• PERMANOVA using the Freedman-Lane permutation scheme,

• PERMANOVA omnibus test using multiple matrices, and

• analytical approach to approximating PERMANOVA p-value.

Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.

The purpose of this package is to factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions is made available with R_RegisterCCallable().