Open Source R Software - Page 4
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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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Yeastar: Business Phone System and Unified CommunicationsUser-friendly, optimized, and scalable, the Yeastar P-Series Phone System redefines business connectivity by bringing together calling, meetings, omnichannel messaging, and integrations in one simple platform—removing the limitations of distance, platforms, and systems.
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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. -
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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. -
Network Management Software and Tools for Businesses and Organizations | Auvik NetworksReduce IT headaches and save time with a proven solution for automated network discovery, documentation, and performance monitoring. Choose Auvik because you'll see value in minutes, and stay with us to improve your IT for years to come.
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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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MetBrewer
Color palette package inspired by Metropolitan Museum of Art in NY
MetBrewer is an R package that provides color palettes inspired by artworks and collections in the Metropolitan Museum of Art (The Met). The idea is to draw on the rich visual heritage of fine art to generate palettes that are aesthetically pleasing and grounded in real-world artistic color usage. The palettes are curated, named after artworks or styles, and often include notes about colorblind-friendliness and contrast. The package supports both discrete and continuous palette types, with interpolation when more colors are requested than originally defined. It also provides ggplot2-friendly scale functions (scale_color_met_c, scale_fill_met_d, etc.) so integration into typical R plotting workflows is smooth. Internally, the package includes functions to list available palettes, check which are colorblind-friendly, and visualize all palettes at once. -
Teradata VantageCloud Enterprise is a data analytics platform for performing advanced analytics on AWS, Azure, and Google Cloud.VantageCloud is the complete cloud analytics and data platform, delivering harmonized data and Trusted AI for all. Built for performance, flexibility, and openness, VantageCloud enables organizations to unify diverse data sources, run complex analytics, and deploy AI models—all within a single, scalable platform.
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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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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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R Color Palettes
Comprehensive list of color palettes available in R
This repository is a curated collection of color palettes crafted or curated for data visualization in R. The goal is to provide designers, data scientists, and R users with aesthetically pleasing, perceptually consistent color schemes that work well for plots, maps, and graphics. The repo contains static files listing palette definitions (e.g. hex codes, named hues), sample visualizations showing how each palette performs under different contexts (categorical, sequential, diverging), and helper functions/scripts to import or use the palettes in R. The author also documents palette provenance and usage guidance (contrast, readability, colorblind friendliness). While not a full package in itself, it’s often used as a reference or source of palette definitions for other R plotting or theming packages. -
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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. -
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RNAseq Tutorial
Informatics for RNA-seq: A web resource for analysis on the cloud
rnaseq_tutorial is a tutorial and educational resource created by the Griffith Lab that guides users through the steps of RNA-seq data analysis. It includes working pipelines for alignment, differential expression, alternative splicing, visualization, and interpretation. It is designed to run in the cloud or local environments, providing introductory material on file formats, reference genomes / annotation, QC, mapping, quantifying expression, visualizing results, etc. The version in that repo is deprecated, but still maintains content for those wishing to follow the original published workflow. Includes instruction on cloud computing basics and using cloud environments for large data processing. -
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Reproducible-research
A Reproducible Data Analysis Workflow with R Markdown, Git, Make, etc.
In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible. -
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Scripting Language Bindings
A port of WFOPT to the several scripting languages
This project contains bindings for various scripting languages to the Wheefun Options Parsing Library. It is meant to provide parity with the C implementation so .NET languages can take advantage of WFOPT. For more information, please see the main page. -
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Seurat
R toolkit for single cell genomics
Seurat is a comprehensive R toolkit for single-cell genomics analysis, introduced by the Satija Lab at NYGC. It supports quality control, normalization, clustering, integration of multimodal data (e.g., scRNA‑seq, spatial, CITE‑seq), and visualization. Seurat v5 introduces scalable workflows and spatial transcriptomics support, commonly used in academic and industry research for single-cell studies. -
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Shiny
Build interactive web apps directly from R with Shiny framework
Shiny is an R package from RStudio that enables users to build interactive web applications using R without requiring knowledge of JavaScript, HTML, or CSS. It allows statisticians and data scientists to turn their analyses into fully functional web dashboards with reactive elements, data inputs, visualizations, and controls, making data communication more effective and dynamic. -
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Statistical Rethinking 2024
This course teaches data analysis
The 2024 repository is the most recent version of the course, reflecting ongoing refinements in pedagogy, statistical modeling techniques, and coding practices. It provides updated notebooks, R scripts, and model examples, some streamlined and restructured compared to previous years. The 2024 repo also highlights the transition toward more robust Stan models and integration with newer Bayesian workflow practices, continuing to emphasize accessibility for learners while modernizing the tools. This version is designed for students following the 2024 lecture series, offering the most current set of examples, exercises, and teaching material aligned with the Statistical Rethinking framework. Online, flipped instruction. I will pre-record the lectures each week. We'll meet online once a week for an hour to discuss the material. The discussion time (3-4pm Berlin Time) should allow people in the Americas to join in their morning. -
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Statistics for Data Scientists
"Statistics for Data Scientists: 50 Essential Concepts"
The “statistics-for-data-scientists” repository is a pedagogical resource designed to bridge rigorous statistics theory and practical data science workflows. The code and materials are intended to help data scientists and analysts grasp statistical principles (e.g. inference, regressions, hypothesis testing, probability, confidence intervals) in contexts relevant to real data analysis tasks. The repository includes Jupyter notebooks, R scripts, worked examples, and possibly problem sets that illustrate how statistical methods are applied to real datasets. It aims to demystify the bridge between textbook statistics and empirical modeling by walking through assumption checking, visualization, interpreting outputs, and pitfalls of misuse. Throughout, the content emphasizes clarity and accessibility, showing not just how to run statistical tests or build models, but what they mean and when one method is preferred over another. -
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TOFSIMS
R/Bioconductor toolkit for mass spectrometry data
The tofsims project is an R/Bioconductor toolkit designed for processing, analyzing, and visualizing imaging mass spectrometry data from Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) instruments. It supports importing raw and preprocessed data from popular instrument platforms (e.g. IONTOF, Ulvac-Phi) and provides methods for mass calibration, peak picking, and peak integration. The package allows transformation of spectra into 2D image structures (mass images), with operations such as binning, scaling, subsetting, and visual rendering. For data exploration and dimensionality reduction, it includes multivariate methods common in the ToF-SIMS community: PCA (Principal Component Analysis), MCR (Multivariate Curve Resolution), MAF (Maximum Autocorrelation Factors), and MNF (Minimum Noise Fraction). It also interoperates with Bioconductor’s imaging stack (e.g. EBImage) so users can apply segmentation and image analysis operations on mass images. -
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Vascular prediction
Study develops predictive model to reduce 90-day readmissions.
In recent years, hospital readmissions have increased, affecting public evaluations and pay-for-performance measures. A study developed a predictive model for 90-day hospital readmission in patients undergoing elective vascular procedures. The best predictive model was Shrinkage Discriminant Analysis, which considered variables such as length of stay, comorbidity scores, procedure type, and admission type. The model indicates that efforts to reduce vascular readmissions should prioritize emergency procedures. This risk stratification allows for better identification and prevention of unnecessary readmissions, crucial in an environment where preventing unplanned readmissions is increasingly important. -
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Wes Anderson Palettes
A Wes Anderson color palette for R
Tired of generic mass produced palettes for your plots? Short of adding an owl and dressing up your plot in a bowler hat, here’s the most indie thing you can do to one. The first round of palettes derived from the amazing Tumblr blog Wes Anderson Palettes.