Showing 200 of total 5586 results (show query)

rstudio

rmarkdown:Dynamic Documents for R

Convert R Markdown documents into a variety of formats.

Maintained by Yihui Xie. Last updated 2 hours ago.

literate-programmingmarkdownpandocrmarkdown

2.9k stars 21.80 score 14k scripts 3.7k dependents

r-lib

devtools:Tools to Make Developing R Packages Easier

Collection of package development tools.

Maintained by Jennifer Bryan. Last updated 6 months ago.

package-creation

2.4k stars 19.55 score 51k scripts 150 dependents

rstudio

htmltools:Tools for HTML

Tools for HTML generation and output.

Maintained by Carson Sievert. Last updated 11 months ago.

218 stars 17.61 score 10k scripts 4.5k dependents

r-lib

profvis:Interactive Visualizations for Profiling R Code

Interactive visualizations for profiling R code.

Maintained by Hadley Wickham. Last updated 6 months ago.

310 stars 15.64 score 1.3k scripts 153 dependents

datastorm-open

visNetwork:Network Visualization using 'vis.js' Library

Provides an R interface to the 'vis.js' JavaScript charting library. It allows an interactive visualization of networks.

Maintained by Benoit Thieurmel. Last updated 2 years ago.

550 stars 15.25 score 4.1k scripts 196 dependents

quarto-dev

quarto:R Interface to 'Quarto' Markdown Publishing System

Convert R Markdown documents and 'Jupyter' notebooks to a variety of output formats using 'Quarto'.

Maintained by Christophe Dervieux. Last updated 14 days ago.

147 stars 14.98 score 1.3k scripts 36 dependents

davidgohel

ggiraph:Make 'ggplot2' Graphics Interactive

Create interactive 'ggplot2' graphics using 'htmlwidgets'.

Maintained by David Gohel. Last updated 3 days ago.

libpngcpp

822 stars 14.37 score 4.1k scripts 35 dependents

rstudio

chromote:Headless Chrome Web Browser Interface

An implementation of the 'Chrome DevTools Protocol', for controlling a headless Chrome web browser.

Maintained by Garrick Aden-Buie. Last updated 5 days ago.

164 stars 13.96 score 162 scripts 28 dependents

bioc

mixOmics:Omics Data Integration Project

Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.

Maintained by Eva Hamrud. Last updated 3 days ago.

immunooncologymicroarraysequencingmetabolomicsmetagenomicsproteomicsgenepredictionmultiplecomparisonclassificationregressionbioconductorgenomicsgenomics-datagenomics-visualizationmultivariate-analysismultivariate-statisticsomicsr-pkgr-project

185 stars 13.75 score 1.3k scripts 22 dependents

christophergandrud

networkD3:D3 JavaScript Network Graphs from R

Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey graphs from 'R'.

Maintained by Christopher Gandrud. Last updated 6 years ago.

d3jsnetworks

654 stars 13.60 score 3.4k scripts 31 dependents

lchiffon

wordcloud2:Create Word Cloud by htmlWidget

A fast visualization tool for creating wordcloud by using wordcloud2.js.

Maintained by Dawei Lang. Last updated 7 years ago.

402 stars 13.10 score 2.8k scripts 11 dependents

johncoene

waiter:Loading Screen for 'Shiny'

Full screen and partial loading screens for 'Shiny' with spinners, progress bars, and notifications.

Maintained by John Coene. Last updated 12 months ago.

hacktoberfestshiny

496 stars 12.87 score 702 scripts 68 dependents

irkernel

repr:Serializable Representations

String and binary representations of objects for several formats / mime types.

Maintained by Philipp Angerer. Last updated 8 months ago.

53 stars 12.35 score 2.2k scripts 45 dependents

ebailey78

shinyBS:Extra Twitter Bootstrap Components for Shiny

Adds easy access to additional Twitter Bootstrap components to Shiny.

Maintained by Eric Bailey. Last updated 9 years ago.

183 stars 12.19 score 3.1k scripts 101 dependents

rstudio

shinytest2:Testing for Shiny Applications

Automated unit testing of Shiny applications through a headless 'Chromium' browser.

Maintained by Barret Schloerke. Last updated 5 days ago.

cpp

108 stars 12.13 score 704 scripts 1 dependents

trestletech

shinyAce:Ace Editor Bindings for Shiny

Ace editor bindings to enable a rich text editing environment within Shiny.

Maintained by Vincent Nijs. Last updated 2 months ago.

222 stars 11.86 score 388 scripts 64 dependents

bioc

bumphunter:Bump Hunter

Tools for finding bumps in genomic data

Maintained by Tamilselvi Guharaj. Last updated 5 months ago.

dnamethylationepigeneticsinfrastructuremultiplecomparisonimmunooncology

16 stars 11.61 score 210 scripts 43 dependents

functionaldata

fdapace:Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Maintained by Yidong Zhou. Last updated 9 months ago.

cpp

31 stars 11.54 score 474 scripts 25 dependents

bioc

systemPipeR:systemPipeR: Workflow Environment for Data Analysis and Report Generation

systemPipeR is a multipurpose data analysis workflow environment that unifies R with command-line tools. It enables scientists to analyze many types of large- or small-scale data on local or distributed computer systems with a high level of reproducibility, scalability and portability. At its core is a command-line interface (CLI) that adopts the Common Workflow Language (CWL). This design allows users to choose for each analysis step the optimal R or command-line software. It supports both end-to-end and partial execution of workflows with built-in restart functionalities. Efficient management of complex analysis tasks is accomplished by a flexible workflow control container class. Handling of large numbers of input samples and experimental designs is facilitated by consistent sample annotation mechanisms. As a multi-purpose workflow toolkit, systemPipeR enables users to run existing workflows, customize them or design entirely new ones while taking advantage of widely adopted data structures within the Bioconductor ecosystem. Another important core functionality is the generation of reproducible scientific analysis and technical reports. For result interpretation, systemPipeR offers a wide range of plotting functionality, while an associated Shiny App offers many useful functionalities for interactive result exploration. The vignettes linked from this page include (1) a general introduction, (2) a description of technical details, and (3) a collection of workflow templates.

Maintained by Thomas Girke. Last updated 5 months ago.

geneticsinfrastructuredataimportsequencingrnaseqriboseqchipseqmethylseqsnpgeneexpressioncoveragegenesetenrichmentalignmentqualitycontrolimmunooncologyreportwritingworkflowstepworkflowmanagement

53 stars 11.52 score 344 scripts 3 dependents

bioc

annotate:Annotation for microarrays

Using R enviroments for annotation.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

annotationpathwaysgo

11.41 score 812 scripts 239 dependents