This package provides a Cairo graphics device that can be use to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG, JPEG, TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don't have Cairo's capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported.

This package implements S4 classes and various tools for financial time series. Basic functions such as scaling and sorting, subsetting, mathematical operations and statistical functions are provided.

This package lets you record test suite HTTP requests and replay them during future runs. It works by hooking into the webmockr R package for matching HTTP requests by various rules, and then caching real HTTP responses on disk in cassettes. Subsequent HTTP requests matching any previous requests in the same cassette use a cached HTTP response.

Plyr is a set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to fit a model to each spatial location or time point in your study, summarise data by panels or collapse high-dimensional arrays to simpler summary statistics.

This package provides functions for the input/output and visualization of medical imaging data that follow either the ANALYZE, NIfTI or AFNI formats. This package is part of the Rigorous Analytics bundle.

RcppDist provides a header-only C++ library with functions for additional statistical distributions that can be called from C++ when writing code using Rcpp or RcppArmadillo. Functions are available that return a NumericVector as well as doubles, and for multivariate or matrix distributions, Armadillo vectors and matrices.

This package lets you compute the median ranking according to Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. The package also provides some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient.

This package implements heuristics for the quadratic assignment problem (QAP). Currently only a simulated annealing heuristic is available.

This package provides fast implementations of functional enrichment analysis methods using C++ via Rcpp. It currently provides Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). The multilevel GSEA algorithm is derived from the fgsea package. Methods are described in Subramanian et al. (2005) <doi:10.1073/pnas.0506580102> and Korotkevich et al. (2021) <doi:10.1101/060012>.

This package provides helpers for reordering factor levels (including moving specified levels to front, ordering by first appearance, reversing, and randomly shuffling), and tools for modifying factor levels (including collapsing rare levels into other, "anonymizing", and manually "recoding").

The bit64 package provides serializable S3 atomic 64 bit (signed) integers that can be used in vectors, matrices, arrays and data.frames. Methods are available for coercion from and to logicals, integers, doubles, characters and factors as well as many elementwise and summary functions. Many fast algorithmic operations such as match and order support interactive data exploration and manipulation and optionally leverage caching.

This package provides tools to compute marginal effects from statistical models and return the result as tidy data frames. These data frames are ready to use with the ggplot2 package. Marginal effects can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The two main functions are ggpredict() and ggeffect(). There is a generic plot() method to plot the results using ggplot2.

This is a package for the analysis of music and speech. Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read MP3, read MIDI, perform steps of a transcription, ...

This package provides improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.

This package provides an efficient interface to MPI by utilizing S4 classes and methods with a focus on Single Program/Multiple Data (SPMD) parallel programming style, which is intended for batch parallel execution.

This package provides algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mapping such as the EM algorithm. It can be used to accelerate any smooth, linearly convergent acceleration scheme. A tutorial style introduction to this package is available in a vignette.

This package provides tools for performing the leaf reordering for the dendrogram that preserves the hierarchical clustering result and at the same time tries to group instances from the same class together.

This package provides functions to access Twitter's filter, sample, and user streams, and to parse the output into data frames.

This package lets you fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models.

This package performs search for the global minimum of a very complex non-linear objective function with a very large number of optima.

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

This package simulates the process of installing a package and then attaching it. This is a key part of the devtools package as it allows you to rapidly iterate while developing a package.

This package provides a toolset for functional enrichment analysis and visualization, gene/protein/SNP identifier conversion and mapping orthologous genes across species via g:Profiler. The main tools are:

1. g:GOSt, functional enrichment analysis and visualization of gene lists;

2. g:Convert, gene/protein/transcript identifier conversion across various namespaces;

3. g:Orth, orthology search across species;

4. g:SNPense, mapping SNP rs identifiers to chromosome positions, genes and variant effects.

This package is an R interface corresponding to the 2019 update of g:Profiler and provides access to versions e94_eg41_p11 and higher.

This package provides functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>. Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2.