Open Source S/R Software - Page 4
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S/R Software
Browse free open source S/R Software and projects below. Use the toggles on the left to filter open source S/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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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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CoLabR
A simple user interface to the R software environment.
A simple user interface to the R software environment. It is designed for people new to R and provides the user with easy access to their variables and data along with pointers to the most basic commands. It also allows for multiple people to share data across different sessions on different machines. In this way it also offers a collaborative environment for people to work together in remote locations. -
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Community Detection Modularity Suite
Suite of community detection algorithms based on Modularity
- MixtureModel_v1r1: overlapping community algorithm [3], which includes novel partition density and fuzzy modularity metrics. - OpenMP versions of algorithms in [1] are available to download. - Main suite containing three community detection algorithms based on the Modularity measure containing: Geodesic and Random Walk edge Betweenness [1] and Spectral Modularity [2]. Collaborator: Theologos Kotsos. [1] M. Newman & M. Girvan, Physical Review, E 69 (026113), 2004. [2] M. Newman, Physical Review E, 74(3):036104, 2006. [3] B. Ball et al, An efficient and principled method for detecting communities in networks, 2011. The suite is based upon the fast community algorithm implemented by Aaron Clauset <aaron@cs.unm.edu>, Chris Moore, Mark Newman, and the R IGraph library Copyright (C) 2007 Gabor Csardi <csardi@rmki.kfki.hu>. It also makes of the classes available from Numerical Recipies 3rd Edition W. Press, S. Teukolsky, W. Vetterling, B. Flanne -
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ConDEnSE (Confidential Data Enabled Statistical Exploration) will be a web-based environment for statistical analysis of confidential data from various database sources, based on Plone and R, and using the Jackknife method of confidentiality protection.
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Corbata
CORe microBiome Analysis Tools
Corbata is a set of statistical tools that can be used to analyze the core microbiome across a set of samples. -
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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CyberUnits
Class library for computational cybernetics
CyberUnits is a cross-platform class library for rapid development of high-performance computer simulations in life sciences. It supports modelling for biomedical cybernetics and systems biology with Object Pascal. -
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Cygwin Ports
Repository of desktops and programs for the Cygwin platform
A large repository of open-source programs built for Cygwin, including X11 desktops, language interpreters, multimedia frameworks, cross-compiler toolchains, and much more. Also hosted here is the cygport tool for building Cygwin packages. cygport releases and the Ports Git repositories are hosted here; Ports packages are available from the website. -
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DACAClus
Algoritmo divisivo de clustering.
Algoritmo divisivo de clustering con determinación automática de componentes. -
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DEAPathways
Differential Expression Analysis for Pathways
This project contains the source code associated with the PLoS Computational Biology publication: "Differential Expression Analysis for Pathways". The paper text can be found here: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002967 -
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DESN
Differential activE sub-Network (DEN): R/Bioconductor based package
Living cells are complex, dynamic, self-regulatory, interactive systems, showing differential states across time and space. Complexity of cellular systems is highlighted with the multi-layered regulatory mechanisms involving the interactions between bio-molecules (such as DNA, RNA, mi-RNA and proteins). These interactions are analyzed in the form of static networks. Likewise, number of experimental techniques like microarray, RNASeq allows quantification of cellular dynamics and aid in discerning differential gene expression across diverse conditions. Computational biology is in need of methods for integration of static networks and gene expression data, since it provides interesting insights into the dynamics of biological systems. DEN is an R/Bioconductor based package designed to assemble different types of human bio-molecular interactions as a complete interactome and contains functions to extract dynamic active networks by integration of gene expression data. -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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The Dataverse Network is a Java EE5 app that enables researchers to share data on-line. It provides data citation standards, facilitates preservation, distribution and replication of data, and includes statistical analysis. More at: http://thedata.org
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Deem
Analyze time-course data with significance tests, clustering, modeling
Use statistical methods to analyze time-course data (gene expression microarray and RNA-seq data in particular, but not limited to). Apply significance tests to filter out only significant genes or time series. Cluster time series into similar groups. Generate network models, including linear or non-linear models. Variable selection and optimization routines included. Written in Scala and R. The application is a cross-platform desktop app with a simple GUI and is fully functional currently. The app was and is developed at the University of Rochester (http://cbim.urmc.rochester.edu) under the GPL 3.0 license. This is a fork of that project. -
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Digital Expression on the Web
DEW is a platform that allows users to explore RNA-Seq data
DEW is a platform that allows users to explore RNA-Seq data. A web-based Graphical User Interface is included. The analysis proceeds as such: gapped alignments are performed and corrected for length, PCR and fragment bias so that a Fragment Per (effective) Kilobase per Million of reads (FPKM) is estimated as well as the simpler Reads Per Kb per Million of reads (RPKM). When provided with multiple isoforms and in the ‘contextual’ mode, corrections include a expectation maximization algorithm estimates effective expression profiles and a corrected alignment is produced. For each gene the user provides, DEW computes coverage descriptive statistics (RPKM, FKMP and total, mean and median corrected counts), expression profiles (normalized as R/FPKM and trimmed mean of fold change). DEW allows users to explore the data by providing interactive graphs. -
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Django-dataplot enables developers using the Django web framework to seamlessly integrate data-driven graphical plots into their web pages.
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DuffyRNAseq is an R package that implements an analysis pipeline for processing RNA-seq data from Illumina NextGen sequencers, to measure gene transcription and differential expression.
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A Python library bringing together utilities I've written over the years for work in bioinformatics and biostatistics, which should be generally applicable outside these fields as well. Focuses on string processing, DBI, and math.
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EDDA
Experiment Design for Differential Abundance Analysis
EDDA is a tool for systematic assessment of the impact of experimental design and the statistical test used on the ability to detect differential abundance. EDDA can aid in the design of a range of common experiments such as RNA-seq, ChIP-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. More details of EDDA can be found at Luo, Huaien et al. “The Importance of Study Design for Detecting Differentially Abundant Features in High-Throughput Experiments.” Genome Biology 2014;15(12):527 (http://www.ncbi.nlm.nih.gov/pubmed/25517037/). An accompanying web server (http://edda.gis.a-star.edu.sg/) is available for easy access to some functionality of EDDA. Additionally a Bioconductor package (http://www.bioconductor.org/packages/release/bioc/html/EDDA.html) is available for easy installation of EDDA R package. -
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My goal is a library for R (www.r-project.org) which encodes concepts relevant to statistical analysis in ecology in relatively easy scalable functions. Includes hierarchical scaling of predictors, density calculations, lme4 model sets, etc...
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EigenMS is a normalization method implemented in R (and older version in Matlab) available as a set of two functions that should be used in a sequence. Please download EigenMS.zip file (latest version). Latest version uploaded in October 2017 has a bugfix for single treatment group normalization. Rescaling has been omitted from 2015. EigenMS utilizes SVD to detect bias trends in the data and eliminates them. EigenMS eliminates effects from known and unknown factors and can be utilized for any -omic platform. We have shown its utility in LC-MS/MS and metabolomics in the following two papers: 1) PMID: 19602524. "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition". Karpievitch YV, Taverner T, Adkins JN, Callister SJ, Anderson GA, Smith RD, Dabney AR. Bioinformatics 2009 2) "Metabolomics data normalization with EigenMS" Karpievitch YV, Nikolic SB, Wilson R, Sharman JE, Edwards LM. PLoS One 2014
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Electrophysiology & circular stats tools
Data analysis and circular statistics with OpenElectrophy and R
Set of tools for basic analysis of electrophysiological data. The Python classes show how to call OpenElectrophy functions and save data. The R library applies circular statistics to spike phase data and saves the best von Mises fit and the Rayleigh statistics on the disk. The wavelet coherence analysis is done in R by the package "sowas". Check the module R_coherence to see how we solved that problem. This packages may be useful for people who start using OpenElectrophy and circular statistics in R. If you find errors, please report them. The project is still in development, so in the future you'll get updates. -
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ElicitN
Interface for expert elicitation of numbers
ElicitN uses R and a tcltk gui interface for eliciting a number from an appropriate expert. The code has been modified from it's original version designed specifically for eliciting the number of species on coral reefs from expert taxonomists. For more information please refer to: http://dx.doi.org/10.1016/j.envsoft.2011.11.011 Please also refer to GitHub for a even simpler more recently maintained version of the project: https://github.com/beckyfisher/ElicitN -
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EnvStats
An R Package for Environmental Statistics
EnvStats is an R package for environmental statistics. It is the open-source successor to the commercial module for S-Plus© called "EnvironmentalStats for S-Plus", which was first released in April, 1997. The EnvStats package, along with the R software environment, provides comprehensive and powerful software for environmental data analysis. EnvStats brings the major environmental statistical methods found in the literature and regulatory guidance documents into one statistical package, along with an extensive hypertext help system that explains what these methods do, how to use these methods, and where to find them in the environmental statistics literature. Also included are numerous built-in data sets from regulatory guidance documents and the environmental statistics literature. EnvStats combined with other R packages (e.g., for spatial analysis) provides the environmental scientist, statistician, researcher, and technician with tools to “get the job done!”
