Open Source R Software for Windows - Page 3
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R Software for Windows
View 26156 business solutionsBrowse free open source R Software for Windows and projects below. Use the toggles on the left to filter open source R Software for Windows 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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Dominate AI Search ResultsAthenaHQ is a cutting-edge platform for Generative Engine Optimization (GEO), designed to help brands optimize their visibility and performance across AI-driven search platforms like ChatGPT, Google AI, and more.
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palmerpenguins
A great intro dataset for data exploration & visualization
palmerpenguins is an R package offering real-world ecological data from the Palmer Archipelago penguin species—Adélie, Chinstrap, and Gentoo. Designed as a more engaging alternative to the classical iris dataset, it provides size measurements, clutch information, and blood isotope data for teaching, visualization, and analytics practice. -
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performance
Models' quality and performance metrics (R2, ICC, LOO, AIC, BF, ...)
performance is part of the easystats ecosystem and offers model quality assessment tools for R. It computes metrics like R², RMSE, ICC, and conducts diagnostics such as overdispersion, zero‑inflation, convergence, and singularity checks, complementing model workflows with comprehensive evaluation. -
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posterdown
Use RMarkdown to generate PDF Conference Posters via HTML
Welcome to Posterdown! This is my attempt to provide a semi-smooth workflow for those who wish to take their RMarkdown skills to the conference world. Many creature comforts from RMarkdown are available in this package such as Markdown section notation, figure captioning, and even citations like this one (Allaire, Xie, McPherson, et al. 2018). The rest of this example poster will show how you can insert typical conference poster features into your own document. Posterdown was created as a proof-of-concept (to myself) that it is possible to make a beautiful poster using open-source reproducible code. -
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rayshader
R Package for 2D and 3D mapping and data visualization
This is an R package designed for producing beautiful and interactive 2D and 3D visualizations — especially maps and terrain renderings — using elevation/gridded data and ray-tracing / hill-shading methods. At its core, rayshader takes a matrix of elevations and applies shading, texture, ambient occlusion, overlays, and light modeling (ray shade, lambertian shading, etc.) to produce realistic relief maps. Users can rotate, zoom, and animate the scenes or script camera trajectories programmatically. It supports outputting high-quality renders via path tracing (using a companion package) and also offers depth-of-field (“cinematic blur”) effects to bring visual focus into scenes. It allows layering relational data (roads, points, polygons) on top of the shaded terrain, so you can combine spatial data overlays with the 3D model. The package can export models to 3D formats like STL or OBJ for 3D printing or external rendering. -
AI-based, Comprehensive Service Management for Businesses and IT ProvidersChangeGear provides IT staff with the functions required to manage everything from ticketing to incident, change and asset management and more. ChangeGear includes a virtual agent, self-service portals and AI-based features to support analyst and end user productivity.
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reticulate
R Interface to Python
reticulate is an R package from Posit that creates seamless interoperability between R and Python. It lets you call Python modules, classes, and functions from within R, automatically translating between R and Python data structures. Useful for combining Python tooling with R projects, data analysis, and RMarkdown reports. -
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sparklyr
R interface for Apache Spark
sparklyr is an R package that provides seamless interfacing with Apache Spark clusters—either local or remote—while letting users write code in familiar R paradigms. It supplies a dplyr-compatible backend, Spark machine learning pipelines, SQL integration, and I/O utilities to manipulate and analyze large datasets distributed across cluster environments. -
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stat-cookbook
The probability and statistics cookbook
A compact “Probability and Statistics Cookbook” offering concise mathematical recipes for key statistical concepts—expectation, variance, distributions and inequalities—packaged as LaTeX and R-based executable documents. -
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swirl
Learn R, in R
swirl is an R package that allows interactive, in-R learning of statistics, data science, R programming etc. The idea is that you load swirl in R, and it presents you with lessons (within R’s console or RStudio) that ask you to type commands, check results, and progress through tutorial material—without leaving the R environment. It is used for teaching R, especially for beginners, as well as for self-paced learning of packages, data manipulation, visualization, etc. Lessons and content are stored locally or can be downloaded and used without a continuous internet connection. Content includes quizzes, multiple-choice questions, coding exercises etc. to reinforce learning. -
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tidyverse
Easily install and load packages from the tidyverse
tidyverse is a meta‑package that installs and loads a cohesive suite of R packages designed for data science, sharing underlying design principles, grammar, and data structures. Core components include ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats, and more. It promotes tidy data workflows and consistency across tasks. -
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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MetEx
MetEx is a computational tool for metabolite targered extraction and a
Liquid chromatography–high resolution mass spectrometry (LC-HRMS) is the most popular platform for untargeted metabolomics methods, but annotating LC-HRMS data is a long-standing bottleneck that we are facing since years ago in metabolomics research. A wide variety of methods have been established to deal with the annotation issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for metabolomics and exposome community. So we developed a user-friendly and powerful software/webserver, MetEx, to both enable implementation of classical peak detection-based annotation and a new annotation method based on targeted extraction algorithms. The new annotation method based on targeted extraction algorithms can annotate more than 2 times metabolites than classical peak detection-based annotation method because it reduces the loss of metabolite signal in the data preprocessing process. -
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic. Being data-aware involves connecting a language model to other sources of data, enabling a comprehensive understanding and analysis of information. -
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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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activAnalyzer is a Shiny app that has been developed to analyze daily physical behavior data recorded at the hip in adults using an ActiGraph accelerometer (.agd file from a GT3X, GT3X+, wGT3X+ or wGT3X-BT device). Once analysis is completed, the app allows exporting results to .csv files and generating a report of the measurement (in either an .html format or a .pdf format). All the configured inputs relevant for interpreting the results are recorded in the report. Be sure that the inputs that are configured when generating the report correspond to the analysis that was actually performed (in other words, avoid modifying the inputs after generating satisfactory results). In addition to an analysis of physical behavior, the app also allows to implement the Daily- and Clinical visit-PROactive Physical Activity in COPD (chronic obstructive pulmonary disease) instruments (D-PPAC and C-PPAC). Please read the user’s guide for details about how the app works.
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methylr
a single shiny solution from sequencer data to pathway analysis
Here we introduce methylR, a complete pipeline for the analysis of both 450K and EPIC Illumina arrays which not only offers data visualization and normalization but also provide additional features such as the annotation of the genomic features resulting from the analysis, pairwise comparisons of DMCs with different graphical representation plus functional and pathway enrichment as downstream analysis, all packed in a minimal, elegant and intuitive graphical user interface which brings the analysis of array DNA methylation data. -
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Advanced Shiny
Shiny tips & tricks for improving your apps and solving common problem
The advanced-shiny repository is a curated collection of practical tips, design patterns, and mini Shiny apps focused on solving real-world challenges in R Shiny applications. The author (Dean Attali) collected many of the “harder” or less-documented tricks he uses or encounters frequently—things like controlling UI behavior dynamically, managing reactive logic, optimizing interactivity, and structuring large Shiny codebases. The repo’s structure includes folders of example apps each implementing a specific trick or pattern (e.g. loading spinners, dynamic UI, hiding/showing UI elements, handling file uploads, URL parameter inputs). Each example is runnable so developers can inspect code and behavior side-by-side. The README acts as a “table of contents” linking to example apps and the contexts in which they are useful (beginner, intermediate, advanced). -
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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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CausalImpact
An R package for causal inference in time series
The CausalImpact repository houses an R package that implements causal inference in time series using Bayesian structural time series models. Its goal is to estimate the effect of an intervention (e.g. a marketing campaign, policy change) on a time series outcome by predicting what would have happened in a counterfactual “no intervention” world. The package requires as input a response time series plus one or more control (covariate) time series that are assumed unaffected by the intervention, and it divides the time horizon into “pre-intervention” and “post-intervention” periods. It uses Bayesian modeling to fit a structural time series to the pre-period and extrapolate a counterfactual prediction for the post period, then compares observed vs predicted to infer the causal effect. The package supports plotting, summary tables, and verbal narratives for interpretive reports. -
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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 -
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Data Analysis for the Life Sciences
Rmd source files for the HarvardX series PH525x
This repository holds the R Markdown (.Rmd) source files for the PH525x / HarvardX course series (Data Analysis for the Life Sciences / Genomics) managed by GenomicsClass. It functions as the canonical source for course lab exercises, lecture modules, and reading materials in reproducible format. Students and learners use these R Markdown files to follow along, knit notebooks, run code samples, and complete the lab-based assignments. The repo is licensed under MIT, allowing reuse and modification. It is part of a larger ecosystem: the compiled HTML / book version of the labs is published via a companion “book” repository, which presents a polished, browsable version of the materials. The content covers topics such as data wrangling in R, statistical inference, genomics workflows, Bioconductor packages, and project-based analyses. Because it’s open and modular, contributors can suggest improvements, update modules, or add new exercises. -
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DataScienceR
a curated list of R tutorials for Data Science, NLP
The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting, and reproducible workflows. The repository also shows examples of linking R with external resources — APIs, databases, and file formats — and integrating into larger pipelines. It acts as a learning scaffold for students or beginners transitioning to more advanced data science work in R, offering a hands-on, example-driven approach. The structure encourages modularity, readability, and reproducible practices, making it a useful reference repository for learners and educators alike. -
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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. -
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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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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.