This package provides an interface to Amazon Web Services analytics services, including Elastic MapReduce Hadoop and Spark big data service, Elasticsearch search engine, and more.

With this tool, a user should be able to quickly implement complex random effect models through simple C++ templates. The package combines CppAD (C++ automatic differentiation), Eigen (templated matrix-vector library) and CHOLMOD (sparse matrix routines available from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through BLAS and parallel user templates.

The jsonlite package provides a fast JSON parser and generator optimized for statistical data and the web. It offers flexible, robust, high performance tools for working with JSON in R and is particularly powerful for building pipelines and interacting with a web API. In addition to converting JSON data from/to R objects, jsonlite contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.

Parametric time warping aligns patterns. It aims to put corresponding features at the same locations. The algorithm searches for an optimal polynomial describing the warping. It is possible to align one sample to a reference, several samples to the same reference, or several samples to several references. One can choose between calculating individual warpings, or one global warping for a set of samples and one reference. Two optimization criteria are implemented: RMS error and WCC. Both warping of peak profiles and of peak lists are supported.

This package contains R functions and datasets detailed in the book "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.

This package implements nested cross-validation applied to the glmnet and caret packages. With glmnet this includes cross-validation of elastic net alpha parameter. A number of feature selection filter functions (t-test, Wilcoxon test, ANOVA, Pearson/Spearman correlation, random forest, ReliefF) for feature selection are provided and can be embedded within the outer loop of the nested CV. Nested CV can be also be performed with the caret package giving access to the large number of prediction methods available in caret.

This package contains a number of comparative "phylogenetic" methods, mostly focusing on analysing diversification and character evolution. Contains implementations of "BiSSE" (Binary State Speciation and Extinction) and its unresolved tree extensions, "MuSSE" (Multiple State Speciation and Extinction), "QuaSSE", "GeoSSE", and "BiSSE-ness" Other included methods include Markov models of discrete and continuous trait evolution and constant rate speciation and extinction.

This package is meant to ease the creation of time-to-event (i.e. survival) endpoint figures. The modular functions create figures ready for publication. Each of the functions that add to or modify the figure are written as proper ggplot2 geoms or stat methods, allowing the functions from this package to be combined with any function or customization from ggplot2 and other ggplot2 extension packages.

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 package implements tools designed to collect and organize Twitter data via Twitter's REST and stream Application Program Interfaces (API).

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 tools to export R data as LaTeX and HTML tables.

This package provides implementations of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear models.

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.

This package provides the tools necessary to do non-standard evaluation (NSE) in R.

This package provides an optimization method based on sequential quadratic programming for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver, and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large.

This package provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e., static .html files). It currently supports linked brushing and filtering.

This package computes the Kendall rank correlation and Mann-Kendall trend test.

This package provides alternative statistical methods for meta-analysis, including:

1. bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences

2. heterogeneity tests and measures that are robust to outliers;

3. measures, tests, and visualization tools for publication bias or small-study effects;

4. meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc.;

5. meta-analysis methods for synthesizing proportions;

6. models for multivariate meta-analysis.

This package provides a language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.

This package provides tools for depth functions methodology applied to multivariate analysis. Besides allowing calculation of depth values and depth-based location estimators, the package includes functions or drawing contour plots and perspective plots of depth functions. Euclidean and spherical depths are supported.

This package implements multiple performance measures for supervised learning. It includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are.

The Rcpp package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about Rcpp is provided by several vignettes included in this package, via the Rcpp Gallery site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, JSS), and the book by Eddelbuettel (2013, Springer); see citation("Rcpp") for details on these last two.

This package provides implementations of the family of map() functions from the purrr package that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.