Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 45 packages in 0.01 seconds

shinydashboard — by Winston Chang, 8 months ago

Create Dashboards with 'Shiny'

Create dashboards with 'Shiny'. This package provides a theme on top of 'Shiny', making it easy to create attractive dashboards.

shiny — by Winston Chang, 8 months ago

Web Application Framework for R

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.

pool — by Joe Cheng, a year ago

Object Pooling

Enables the creation of object pools, which make it less computationally expensive to fetch a new object. Currently the only supported pooled objects are 'DBI' connections.

learnr — by Barret Schloerke, 2 years ago

Interactive Tutorials for R

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.

provParseR — by Barbara Lerner, 2 years ago

Pulls Information from Prov.Json Files

R functions to access provenance information collected by 'rdt' or 'rdtLite'. The information is stored inside a 'ProvInfo' object and can be accessed through a collection of functions that will return the requested data. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in < https://github.com/End-to-end-provenance/ExtendedProvJson>.

websocket — by Winston Chang, 9 months ago

'WebSocket' Client Library

Provides a 'WebSocket' client interface for R. 'WebSocket' is a protocol for low-overhead real-time communication: < https://en.wikipedia.org/wiki/WebSocket>.

randtoolbox — by Christophe Dutang, 7 months ago

Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests

Provides (1) pseudo random generators - general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm and WELL generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some generator tests - the gap test, the serial test, the poker test. See e.g. Gentle (2003) . The package can be provided without the rngWELL dependency on demand. Take a look at the Distribution task view of types and tests of random number generators. Version in Memoriam of Diethelm and Barbara Wuertz.

ems — by Lunna Borges, 5 months ago

Epimed Solutions Collection for Data Editing, Analysis, and Benchmark of Health Units

Collection of functions related to benchmark with prediction models for data analysis and editing of clinical and epidemiological data.

provSummarizeR — by Barbara Lerner, 2 years ago

Summarizes Provenance Related to Inputs and Outputs of a Script or Console Commands

Reads the provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output created by the execution of a script, and provides a human-readable summary identifying the input and output files, the script used (if any), errors and warnings produced, and the environment in which it was executed. It can also optionally package all the files into a zip file. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in < https://github.com/End-to-end-provenance/ExtendedProvJson>. More information about 'rdtLite' and associated tools is available at < https://github.com/End-to-end-provenance/> and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, .

provExplainR — by Barbara Lerner, 10 months ago

Compare Provenance Collections to Explain Changed Script Outputs

Inspects provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output created by the execution of a script, and find differences between two provenance collections. Factors under examination included the hardware and software used to execute the script, versions of attached libraries, use of global variables, modified inputs and outputs, and changes in main and sourced scripts. Based on detected changes, 'provExplainR' can be used to study how these factors affect the behavior of the script and generate a promising diagnosis of the causes of different script results. More information about 'rdtLite' and associated tools is available at < https://github.com/End-to-end-provenance/> and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, .