Open Source R Software - Page 3
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R Software
Browse free open source R Software and projects below. Use the toggles on the left to filter open source R Software by OS, license, language, programming language, and project status.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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La version gratuite d'Auth0 s'enrichit !Vous l'avez demandé, nous l'avons fait ! Les versions gratuite et payante d'Auth0 incluent des options qui vous permettent de développer, déployer et faire évoluer vos applications en toute sécurité. Utilisez Auth0 dès maintenant pour découvrir tous ses avantages.
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AnomalyDetection
Anomaly Detection with R
AnomalyDetection is an R package developed by Twitter for detecting anomalies in seasonal univariate time series. It implements the Seasonal Hybrid Extreme Studentized Deviate (S‑H‑ESD) test, which reliably identifies both global and local outliers in data with trends and seasonality—commonly applied to system metrics, engagement data, and business KPIs. -
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Awesome Network Analysis
A curated list of awesome network analysis resources
awesome-network-analysis is a curated list of resources focused on network and graph analysis, including libraries, frameworks, visualization tools, datasets, and academic papers. It covers multiple programming languages and domains like sociology, biology, and computer science. This repository serves as a central reference for researchers, analysts, and developers working with network data. -
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ComplexHeatmap
Make Complex Heatmaps
ComplexHeatmap is an R/Bioconductor package by Zuguang Gu et al. designed to create highly flexible, complex, richly annotated heatmaps and related visualizations. It allows arranging multiple heatmaps, adding annotations, combining heatmaps, customizing colors, layouts, and integrating other plots. Often used in genomics/bioinformatics to show expression, methylation, etc., with sidebars, annotations, clustering, etc. Highly customizable layout: combining different heatmaps, arranging and splitting, dealing with multiple heatmap merges, combining with other plots etc. Integration with Shiny / interactive heatmaps via companion packages (InteractiveComplexHeatmap) to allow interactivity, etc. -
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Covidex
Ultra fast and accurate subtyping tool of viral genomes.
Viral subtypes or clades represent clusters among isolates from the global population of a defined species. Subtypification is relevant for studies on virus epidemiology, evolution and pathogenesis. In this sense, Covidex was developed as an open source alignment-free machine learning subtyping tool. It is a shiny app that allows fast and accurate classification of viral genomes in pre-defined clusters. If more than 1000 sequences are loaded the tool will run in multithread mode. Capable of classifying 16000 genome sequences in less than a minute (AMD Ryzen 7 1700 8-core Processor 3 GHz) For a Web-based version of the app (only for small datasets: 100 seqs max) please go to http://covidex.unlu.edu.ar If you use Covidex please consider citing the following preprint: https://biorxiv.org/cgi/content/short/2020.08.21.261347v1 If you think my work is useful you can buy me a coffee! https://www.buymeacoffee.com/mcacciabue -
Unimus makes Network Automation and Configuration Management easy.We aim to make automation, disaster recovery, change management and configuration auditing painless and affordable for a network of any size.
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DiagrammeR
Graph and network visualization using tabular data in R
DiagrammeR is an R package to create, manipulate, and visualize network graphs, flowcharts, diagrams, and more using Graphviz and Mermaid syntax. Integrates with RMarkdown and Shiny apps, supports node/edge traversal, and graph analysis algorithms, making it ideal for documenting processes, causal relationships, or data pipelines. -
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No-code system is for the visual creation of structural-functional models and the automatic generation of R language simulation models. The program can be used to describe information, production, organizational, and other processes. For graphical representation, the EdPM/EPM notation is used, which allowed us to implement: - structural-functional modeling using graphical methods; - the study of the efficiency of structural-functional models using simulation methods, that allow (e.g. unlike Petri nets) to process queries in groups, which is important for the study of the efficiency of using such methods as volumetric calendar planning and AI methods in process activities, since the operating time of these methods depends on the number of parameters and changes nonlinearly; - the study of multiprocess systems; - the results were obtained, that allow you to find efficient topologies of structural-functional models.
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FAST-GHG
A fast tool to caculatate greenhouse gases in agriculture
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FriendsDon'tLetFriends
Friends don't let friends make certain types of data visualization
Friends don't let friends make certain types of data visualization - What are they and why are they bad. -
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GDINA Package for Cognitively Diagnostic
Package for Cognitively Diagnostic Analyses
Estimating G-DINA model and a variety of widely-used models subsumed by the G-DINA model, including the DINA model, DINO model, additive-CDM (A-CDM), linear logistic model (LLM), reduced reparametrized unified model (RRUM), multiple-strategy DINA model for dichotomous responses. Estimating models within the G-DINA model framework using user-specified design matrix and link functions. Estimating Bugs-DINA, DINO and G-DINA models for dichotomous responses. Estimating sequential G-DINA model for ordinal and nominal responses. Estimating the generalized multiple-strategy cognitive diagnosis models (experimental). Estimating the diagnostic tree model (experimental). Estimating multiple-choice models. Modelling independent, saturated, higher-order, loglinear smoothed, and structured joint attribute distribution. Accommodating multiple-group model analysis. Imposing monotonic constrained success probabilities. Accommodating binary and polytomous attributes. -
OpManager the network monitoring software used by over 1 million IT adminsManageEngine OpManager is a powerful network monitoring software that provides deep visibility into the performance of your routers, switches, firewalls, load balancers, wireless LAN controllers, servers, VMs, printers, and storage devices. It is an easy-to-use and affordable network monitoring solution that allows you to drill down to the root cause of an issue and eliminate it.
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Glycometrics
Algorithms for glycaemic variability
Glycometrics is a collection of algorithms that provide metrics for glycaemic variability. Its application is mainly in theoretical endocrinology and diabetes research. -
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Harmony Data Integration
Fast, sensitive and accurate integration of single-cell data
Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets. It is especially useful for large single-cell datasets such as single-cell RNA-seq. Harmony has been tested on R versions =4. Please consult the DESCRIPTION file for more details on required R packages. Harmony has been tested on Linux, OS X, and Windows platforms. -
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Huxtable
An R package to create styled tables in multiple output formats
Huxtable is an R package to create LaTeX and HTML tables, with a friendly, modern interface. Features include control over text styling, number format, background color, borders, padding, and alignment. Cells can span multiple rows and/or columns. Tables can be manipulated with standard R subsetting or dplyr functions. -
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IRkernel
R kernel for Jupyter
For detailed requirements and install instructions see irkernel.github.io. Per default IRkernel::installspec() will install a kernel with the name “ir” and a display name of “R”. Multiple calls will overwrite the kernel with a kernel spec pointing to the last R interpreter you called that commands from. You can install kernels for multiple versions of R by supplying a name and display name argument to the install spec() call (You still need to install these packages in all interpreters you want to run as a Jupyter kernel!): -
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JuliaConnectoR
A functionally oriented interface for calling Julia from R
This R-package provides a functionally oriented interface between R and Julia. The goal is to call functions from Julia packages directly as R functions. Julia functions imported via the JuliaConnectoR can accept and return R variables. It is also possible to pass R functions as arguments in place of Julia functions, which allows callbacks from Julia to R. From a technical perspective, R data structures are serialized with an optimized custom streaming format, sent to a (local) Julia TCP server, and translated to Julia data structures by Julia. The results of function calls are likewise translated back to R. Complex Julia structures can either be used by reference via proxy objects in R or fully translated to R data structures. -
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KidneyExplorer
Kidney proteomics data explorer enables you to investigate diseases
KidneyExplorer enables you to interactively survey kidney proteomics datasets from different kidney disease models. Here you can download the corresponding SQL database dumps. The original website for the shiny app is: https://kidneyapp.shinyapps.io/kidneyorganoids/ -
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LBopsis
THERE IS A PROBLEM WITH SOME OUTDATED LIBRARIES Work in progress!
Semi-automated workflow to quantify root bending and elongation for scanned pictures of Arabidopsis thaliana -
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MHNs Data Science Examples
Collection of data science examples.
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Mastering Shiny
Mastering Shiny: a book
Mastering Shiny is a book (and its accompanying source repository) by Hadley Wickham that teaches people how to build interactive web applications using Shiny in R. It starts from basics (your first app, UI components, reactivity) and progresses to more advanced topics (dynamic UIs, modules, testing, security, performance). It is intended to help data scientists, analysts, or R users who may not have deep experience in web technologies become expert Shiny developers. The source code is open, and the book is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. -
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OmicSelector
Feature selection and deep learning modeling for omic biomarker study
OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant diagnostic potential (based on the results of miRNA-seq, for validation in qPCR experiments). -
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Open Intro Statistics
An open-source textbook written at the college level
OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League. Each chapter's content is in one of the eight chapter folders that start with "ch_". Within each folder, there is a "figures" folder and a "TeX" folder. The TeX folder contains the text files that are used to typeset the chapters in the textbook. In many cases, R code is supplied with figures to regenerate the figure. It will often be necessary to install the "openintro" R package that is available from GitHub (https://github.com/OpenIntroOrg) if you would like to regenerate a figure. Other packages may also occasionally be required. -
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Pain2D are programs which were developed for the automated pain drawing collection and classification of diseases on the basis of pain drawings in pen-and-paper and digital form for research purposes. Pain2D is currently not a diagnostic tool, but is aimed at scientists, physicians and anyone interested in the automated analysis of pain drawings.
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QBPWCF
PHP library for building website in Fedora Linux
此專案的目的是要建立簡單、易用、參數說明完整且富有調整性的PHP元件庫，讓網頁程式設計開發者可以輕鬆地建立高度客製化的網站。 套用當代的術語而言，就是要作為LOW CODE平台的函式庫。 -
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QuantifyPoly(A)
Quantification of poly(A) sites from 3' end sequencing data
QuantifyPoly(A) - a tool for quantification of poly(A) sites from 3' end sequencing data. [1] QuantifyPoly(A) user manual Please visit the Wiki page of this website. [2] QuantifyPoly(A) Q&A For Q&A, please visit the Blog page of this website. [3] QuantifyPoly(A) bug report You can report a bug as a Ticket request, or start a topic session in the Discussion webpage of this website. -
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R Markdown Cookbook
R Markdown Cookbook
R Markdown Cookbook. A range of tips and tricks to make better use of R Markdown. R Markdown is a powerful tool for combining analysis and reporting into the same document. Since the birth of the rmarkdown package (Allaire, Xie, Dervieux, McPherson, et al. 2023) in early 2014, R Markdown has grown substantially from a package that supports a few output formats, to an extensive and diverse ecosystem that supports the creation of books, blogs, scientific articles, websites, and even resumes. -
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R Packages (r-pkgs)
Building R packages
rpkgs (in GitHub via hadley/r-pkgs) is the source (text + examples) for the book R Packages by Hadley Wickham and Jenny Bryan. The book teaches how to develop, document, test, and share R packages: the practices, tools, infrastructure, workflows, and best practices around package development in R. The repository contains the code, text, site content for building the book, examples, exercises, etc. It is not a software library to be loaded in R (except perhaps the examples), but a resource/guide/manual. The first edition is no longer available online. A second edition is under development and available.