JJ Allaire
38 packages on CRAN
3 packages on GitHub
Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value.
Scientific and technical article format for the web. 'Distill' articles feature attractive, reader-friendly typography, flexible layout options for visualizations, and full support for footnotes and citations.
Interactive plotting functions for use within RStudio. The manipulate function accepts a plotting expression and a set of controls (e.g. slider, picker, checkbox, or button) which are used to dynamically change values within the expression. When a value is changed using its corresponding control the expression is automatically re-executed and the plot is redrawn.
Scientific and technical article format for the web. 'Radix' articles feature attractive, reader-friendly typography, flexible layout options for visualizations, and full support for footnotes and citations.
R Markdown format for 'reveal.js' presentations, a framework for easily creating beautiful presentations using HTML.
Create and manage unique directories for each 'TensorFlow' training run. Provides a unique, time stamped directory for each run along with functions to retrieve the directory of the latest run or latest several runs.
Write blog posts and web pages in R Markdown. This package supports the static site generator 'Hugo' (<https://gohugo.io>) best, and it also supports 'Jekyll' (<http://jekyllrb.com>) and 'Hexo' (<https://hexo.io>).
Output formats and utilities for authoring books and technical documents with R Markdown.
Interface to the Google Cloud Machine Learning Platform <https://cloud.google.com/ml-engine>, which provides cloud tools for training machine learning models.
Data objects in R can be rendered as HTML tables using the JavaScript library 'DataTables' (typically via R Markdown or Shiny). The 'DataTables' library has been included in this R package. The package name 'DT' is an abbreviation of 'DataTables'.
An R interface to the 'dygraphs' JavaScript charting library (a copy of which is included in the package). Provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting.
Format for converting an R Markdown document to a grid oriented dashboard. The dashboard flexibly adapts the size of it's components to the containing web page.
A framework for creating HTML widgets that render in various contexts including the R console, 'R Markdown' documents, and 'Shiny' web applications.
Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.
Create interactive tutorials using R Markdown. Use a combination of narrative, figures, videos, exercises, and quizzes to create self-paced tutorials for learning about R and R packages.
Like package 'manipulate' does for static graphics, this package helps to easily add controls like sliders, pickers, checkboxes, etc. that can be used to modify the input data or the parameters of an interactive chart created with package 'htmlwidgets'.
Provides R bindings to the 'Sundown' Markdown rendering library (<https://github.com/vmg/sundown>). Markdown is a plain-text formatting syntax that can be converted to 'XHTML' or other formats. See <http://en.wikipedia.org/wiki/Markdown> for more information about Markdown.
Manage the R packages your project depends on in an isolated, portable, and reproducible way.
Creating tiny yet beautiful documents and vignettes from R Markdown. The package provides the 'html_pretty' output format as an alternative to the 'html_document' and 'html_vignette' engines that convert R Markdown into HTML pages. Various themes and syntax highlight styles are supported.
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, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013, <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018, <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.
High level functions for parallel programming with 'Rcpp'. For example, the 'parallelFor()' function can be used to convert the work of a standard serial "for" loop into a parallel one and the 'parallelReduce()' function can be used for accumulating aggregate or other values.
Interface to 'Python' modules, classes, and functions. When calling into 'Python', R data types are automatically converted to their equivalent 'Python' types. When values are returned from 'Python' to R they are converted back to R types. Compatible with all versions of 'Python' >= 2.7.
'R' 'Markdown' format for 'shower' presentations, see <https://github.com/shower/shower>.
Programmatic deployment interface for 'RPubs', 'shinyapps.io', and 'RStudio Connect'. Supported content types include R Markdown documents, Shiny applications, Plumber APIs, plots, and static web content.
An extension package for 'sparklyr' that provides an R interface to H2O Sparkling Water machine learning library (see <https://github.com/h2oai/sparkling-water> for more information).
Access the RStudio API (if available) and provide informative error messages when it's not.
A suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.
'R' implementation and interface of the Machine Learning platform 'PyTorch' <https://pytorch.org/> developed in 'Python'. It requires a 'conda' environment with 'torch' and 'torchvision' to provide 'PyTorch' functions, methods and classes. The key object in 'PyTorch' is the tensor which is in essence a multidimensional array. These tensors are fairly flexible to perform calculations in CPUs as well as 'GPUs' to accelerate the process.
Earth Engine <https://earthengine.google.com/> client library for R. All of the 'Earth Engine' API classes, modules, and functions are made available. Additional functions implemented include importing (exporting) of Earth Engine spatial objects, extraction of time series, interactive map display, assets management interface, and metadata display. See <https://r-spatial.github.io/rgee/> for further details.
Comprehensive set of tools for scaffolding R interfaces to modules, classes, functions, and documentations written in other programming languages, such as 'Python'.
Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.
R interface to Apache Spark, a fast and general engine for big data processing, see <http://spark.apache.org>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.
Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
Interface to 'TensorFlow' Datasets, a high-level library for building complex input pipelines from simple, re-usable pieces. See <https://www.tensorflow.org/programmers_guide/datasets> for additional details.
Interface to 'TensorFlow' Estimators <https://www.tensorflow.org/programmers_guide/estimators>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.
'TensorFlow' Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models. A module is a self-contained piece of a 'TensorFlow' graph, along with its weights and assets, that can be reused across different tasks in a process known as transfer learning. Transfer learning train a model with a smaller dataset, improve generalization, and speed up training.
Low-level socket-based interface to calling the Spark API via the RBackend server included in Spark.