Open Source R Software - Page 4
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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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ShinyItemAnalysis
Test and Item Analysis via Shiny
ShinyItemAnalysis is an R package including functions and interactive shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters. Exploration of total and standard scores. Analysis of measurement error and reliability. Analysis of correlation structure and validity. Traditional item analysis. Item analysis with regression models. Item analysis with IRT models. Detection of differential item functioning. Number of toy datasets is available, the interactive application also allows the users to upload and analyze their own data and to automatically generate PDF or HTML reports. All methods include sample R code which is ready to copy and paste into R and run locally. Several toy data sets are ready to use. You can also upload and analyze your own data. ShinyItemAnalysis provides model equations, parameter estimates and their interpretation. -
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Statistical Rethinking 2022
Statistical Rethinking course winter 2022
This repository hosts the 2022 version of the Statistical Rethinking course. It contains course materials such as R scripts, notebooks, and worked examples aligned with McElreath’s textbook. The code emphasizes Bayesian data analysis using R, the rethinking package, and Stan models. It includes lecture code files, example datasets, and structured exercises that parallel the topics covered in the lectures (probability, regression, model comparison, Bayesian updating). The repo functions as a direct hands-on reference for students following the 2022 recorded lecture series. There are 10 weeks of instruction. Links to lecture recordings will appear in this table. Weekly problem sets are assigned on Fridays and due the next Friday, when we discuss the solutions in the weekly online meeting. -
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Statistical Rethinking 2023
Statistical Rethinking Course for Jan-Mar 2023
The 2023 edition modernizes and expands on the same curriculum, adjusting exercises and code for newer versions of R, Stan, and supporting packages. It continues to provide scripts for lectures and tutorials, while integrating refinements to examples, notation, and computational workflows introduced that year. Compared with 2022, some models are rewritten for clarity, and teaching materials reflect refinements in McElreath’s evolving presentation of Bayesian data analysis. Students following the 2023 lecture videos use this repository as their coding reference. There are 10 weeks of instruction. Links to lecture recordings will appear in this table. Weekly problem sets are assigned on Fridays and due the next Friday, when we discuss the solutions in the weekly online meeting. -
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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. -
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adv-r
Advanced R: a book
Source repository for Advanced R, the authoritative guide by Hadley Wickham, built with bookdown (R Markdown) and designed to deepen R users’ understanding of language internals, functional programming, object systems, and metaprogramming. -
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bbplot
R package that helps create and export ggplot2 charts
bbplot is an R package developed by the BBC visual journalism team aimed at helping data journalists and analysts produce chart styles consistent with BBC aesthetics. It provides functions and themes that make it easier to adopt BBC’s visual style (fonts, colors, annotations, layout) in ggplot2 plots. The package includes helper functions for axis labels, captions, legends, branding (e.g. BBC red lines or accents), and common chart types styled for editorial presentation. It offers templates and defaults that reduce styling overhead so users can focus on data and storytelling rather than aesthetic minutiae. Because visual consistency is important in media, bbplot helps non-designers build plots that align with professional publication standards. The repository includes documentation, vignettes, example plots, and guidelines for customization (e.g. switching colors, modifying typography). -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. The repository is structured in logical folders, each corresponding to algorithm categories. -
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blavaan
An R package for Bayesian structural equation modeling
blavaan is a free, open-source R package for Bayesian latent variable analysis. It relies on JAGS and Stan to estimate models via MCMC. The blavaan functions and syntax are similar to lavaan. The development version of blavaan (containing updates not yet on CRAN) can be installed via the command provided in the documentation. Compilation is required; this may be a problem for users who currently rely on a binary version of blavaan from CRAN. The blavaan package depends on the lavaan package for model specification and for some computations. This means that, if you already know lavaan, then you should already be able to do many things in blavaan. In particular, many blavaan commands add the letter “b” to the start of the lavaan command. It is also sometimes possible to use a lavaan command on a blavaan object, though the results may not always be what you expect. -
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blogdown
Create Blogs and Websites with R Markdown
blogdown is an R package that enables the creation and maintenance of static websites and blogs using R Markdown and Hugo (or other static-site generators). Developed by Yihui Xie and team, it provides functions to initialize sites, write posts, manage themes, and deploy with minimal fuss. It seamlessly blends R code chunks and web content, ideal for data storytellers and technical bloggers. -
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bookdownplus
Use R package bookdown for writing varied types of books and documents
The package bookdownplus (Zhao 2017a) is an extension of R bookdown (Xie 2016). It is a collection of multiple templates on the basis of LaTeX, which is tailored so that I can work happily under the umbrella of bookdown. bookdownplus helps you write academic journal articles, guitar books, chemical equations, mails, calendars, and diaries. bookdown features the collaboration of many fantastic tools. However, an R beginner might be confused or depressed in struggling in the flood of LaTeX, YAML, Markdown, Pandoc, etc. It would be a pity if users stopped their steps at the door and gave up the courage to enter the wonderful world of bookdown. bookdownplus is the easiest shortcut to the world of bookdown. With just one single command users can get a demo book (or multiple demo books) in .pdf or .doc, or even more formats. bookdownplus extends the features of bookdown, and simplifies the procedure. -
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box
Write reusable, composable and modular R code
box is an R package providing a modular system / module loader for organizing reusable R code outside of full packages. It allows users to treat R scripts (files/folders) as modules — possibly nested — with explicit exports, imports, and scoping. The idea is to let users structure code in a more modular, composable way, without needing every reusable component to be a full CRAN-style package. It also provides a cleaner syntax for importing functions or modules (via box::use) that allows scoping control and avoids global pollution. Such modules can be stored in a central module search path (configured via options('box.path')) analogous to the R package library, or locally in individual projects. Let’s assume the module we just defined is stored in a file hello_world.r inside a directory mod, which is inside the module search path. -
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brics
www.youtube.com/@Akhona_Kama
BRICS COIN is a decentralized cryptocurrency project designed to empower users in the BRICS nations (Brazil, Russia, India, China, and South Africa) by providing a secure, efficient, and accessible means of digital value transfer and financial inclusion. Built on the Ethereum blockchain as an ERC20 token, BRICS COIN aims to facilitate cross-border transactions, enhance economic collaboration, and foster innovation within the BRICS countries. Key Features Token Functionality: ERC20 Standard: Ensures compatibility with various wallets and decentralized applications (dApps). Minting and Burning: Allows for controlled supply management, enabling the creation of new tokens and the destruction (burning) of tokens to influence market dynamics. Decentralized Finance (DeFi) Functionality: Explore opportunities in DeFi, from staking to yield farming, helping users to earn rewards on their holdings. Community Governance: Empower the BRICS community by enabling token holders to p -
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brms
brms R package for Bayesian generalized multivariate models using Stan
brms is an R package by Paul Bürkner which provides a high-level interface for fitting Bayesian multilevel (i.e. mixed effects) models, generalized linear / non-linear / multivariate models using Stan as the backend. It allows R users to specify complex Bayesian models using formula syntax similar to lme4 but with far more flexibility (distributions, link functions, hierarchical structure, nonlinear terms, etc.). It supports model diagnostics, posterior predictive checking, model comparison, custom priors, and advanced features such as distributional regression. -
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broom
Convert statistical analysis objects from R into tidy format
broom is part of the tidymodels ecosystem that converts statistical model outputs (e.g. from lm, glm, t.test, lme4, etc.) into tidy tibbles — standardized data frames — using functions tidy(), glance(), and augment(). These are easier to manipulate, visualize, and report programmatically. -
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caret
caret (Classification And Regression Training) R package
The caret (Classification And Regression Training) R package streamlines the process of building predictive machine learning models. It provides uniform interfaces for model training, tuning, evaluation, preprocessing, and variable importance. With support for over 200 models, caret is foundational for R workflows in modeling and machine learning. -
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circlize
Circular visualization in R
circlize is an R package for creating circular visualizations (plots laid out in circular coordinate systems) in a very flexible way. It implements many types of plots using circular layouts: chord diagrams, circular heatmaps, arcs/links between sectors, genomic data visualization, etc. It provides low-level drawing functions as well as high-level functions to build complex visualizations. It’s often used in genomics, network analysis, or other fields where relationships among categories or entities can be nicely displayed in a circular fashion. Support for circular heatmaps, multiple tracks (rings), for showing multiple layers of data per sector. Good performance and stable codebase, detailed documentation including a book on usage examples. -
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clusterProfiler
A universal enrichment tool for interpreting omics data
clusterProfiler is an R/Bioconductor package that provides a unified workflow for functional enrichment analysis to interpret high-throughput omics results. It supports both over-representation analysis and gene set enrichment analysis, letting you work with unranked gene lists or ranked statistics from differential pipelines. The package connects to multiple knowledge bases—such as Gene Ontology, KEGG, Reactome, Disease Ontology, MeSH and others—through a consistent interface so you can query different biological lenses without rewriting code. It is designed for breadth, covering coding and non-coding features and thousands of organisms by leveraging continuously updated annotations. Results are returned in tidy, manipulation-friendly structures and pair naturally with rich visualization functions (via companion tooling) to summarize pathways, terms, and gene–set relationships. -
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covid19model
Code for modelling estimated deaths and cases for COVID19
Code for modeling estimated deaths and infections for COVID-19 from "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe", Flaxman, Mishra, Gandy et al, Nature, 2020, the published version of our original Report 13. This is the release related to our Tiers paper, where we use the latent factor model to estimate the effectiveness of tiers systems in England. Peer-reviewed version is to be out soon. All other code is still the same for previous releases. The code should be run in full mode to obtain credible results. Not running a full run to estimate anything is not recommended and discouraged. Only a full run should be used to get results. -
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data.table
Extends base R’s data for high-performance data manipulation
data.table is an R package that extends base R’s data.frame for high-performance data manipulation. It offers concise syntax, blazing speed, and memory-efficient operations. It supports fast file reading/writing, joins, grouping, reshaping, and updates by reference. It is heavily used in large data workflows, big data in R, production pipelines, etc. Extremely efficient grouping/aggregation/summarization; can handle very large datasets (hundreds of millions to billions of rows) in memory (if available). Relies only on base R; maintained API, active community; good memory efficiency. Non-equi joins, overlapping range joins, ordered joins, joining with aggregations, etc.
