Open Source S/R Software for Windows
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S/R Software for Windows
View 26365 business solutionsBrowse free open source S/R Software for Windows and projects below. Use the toggles on the left to filter open source S/R Software for Windows by OS, license, language, programming language, and project status.
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R packages (maintained by YJLEE)
R packages for PK/PD modeling , BE/BA, drug stability, ivivc, etc.
These R packages are developed for data analysis of PK/PD modeling & simulation, bioequivalence/bioavailability (BE/BA), drug stability, in-vitro and in-vivo correlation (ivivc), as well as therapeutic drug monitoring (TDM). -
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bootf2BCA
computing f2 bootstrap CI BCA
Computing similarity factor (f2) bootstrap bias corrected and accelerated confidence interval -
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HeatmapGenerator is a graphical user interface software program written in C++, R, and OpenGL to create customized gene expression heatmaps from RNA-seq and microarray data in medical research. HeatmapGenerator can also be used to make heatmaps in a variety of other non-medical fields. HeatmapGenerator is peer-reviewed published software (http://www.scfbm.org/content/9/1/30). Please cite: [Khomtchouk et al.: "HeatmapGenerator: High performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline." Source Code for Biology and Medicine, 2014 9:30] HeatmapGenerator source code is available at: https://github.com/Bohdan-Khomtchouk/HeatmapGenerator Bohdan Khomtchouk wishes to acknowledge the support of the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. HeatmapGenerator is released under the GNU GPL.
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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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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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GISBox by Citiviz
Ready-to-use appliance for GIS, Data Mining and Analysis, Modelling...
GISBox by Citiviz is a ready-to-use virtual appliance containing an assortment of powerful, free and open source GIS, Data Mining and Analysis, Modelling and Programming software and packages. The pre-configured and tested GISBox allows you to try and use in few minutes a variety of software used by data scientists, engineers, physicists, mathematicians, geomatics engineers, and geographers from around the globe, without installing anything more than once the Oracle VirtualBox™ software (https://www.virtualbox.org/) on your Linux, Windows or Mac computer. The GISBox is free and open, allowing it to be freely distributed, duplicated and passed around, even with your own datasets and in-house applications. If you read this and are related to the Ecole polytechnique de Lausanne - EPFL (http://www.epfl.ch) in Switzerland, please use this link for downloading or support information: http://gisbox.epfl.ch. Happy (geo)Data Hacking! Brought to you by Citiviz' Builders -
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KMWin
Kaplan-Meier for Windows
KMWin (Kaplan-Meier for Windows) is a convenient tool for graphical presentation of results from Kaplan-Meier survival time analysis. The programme is based on the statistical software environment R and provides an easy to use graphical interface. As an introduction, see http://dx.plos.org/10.1371/journal.pone.0038960#s2. -
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Mass-Up
MALDI-TOF data analysis tool
Mass-Up is an Open-Source mass spectrometry utility for proteomics designed to support the preprocessing and analysis of MALDI-TOF mass spectrometry data. Mass-Up includes several tools and operations to load, preprocess and analyze MALDI-TOF data. -
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MoPAC
The Modular Pipeline for the Analysis of CRISPR screens
To facilitate the comparison of gene essentialities in two or more cell samples, we propose MoPAC (Modular Pipeline for Analysis of CRISPR screens), a Shiny-driven interactive tool for differential essentiality analysis in CRISPR/Cas9 screens. For installation and usage instructions please refer to the wiki page. -
Add Two Lines of Code. Get Full APM.Works out of the box for Rails, Django, Express, Phoenix, and more. Monitoring exceptions and performance in no time.
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We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. License: The DSS R-package is made available under the terms of the GNU General Public License, which means that the source code is freely available for use within other software, but if you alter the code and distribute it, you must make the new source code freely available as well. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY. In case you use the package in your work, we do appreciate a citation to a DSS publication. Citation: Yadav et al. (2014), Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports 4, 5193; DOI:10.1038/srep05193 Please visit the link below for details
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YANG (Yet Another Network Generator - Java) enables you to generate social networks given various social rules observed in the real population. Uses: generate realistic networks to be used in individual-centric models, teaching or benchmarking.
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dataMAPPs
R based pipeline for MHC-associated peptide proteomics (MAPPs) data
dataMAPPs allows routine and efficient processing of data from immunogenicity studies applying the MAPPs peptidomics technology to detect potential MHCI- or MHC-II epitopes as presented by dendritic cells (DC). It features quality control of the raw data, across-sample/across-donor normalization and visualization of results in a heatmap style (heatMAPPs). dataMAPPs' core is a generic R library that can be tailored to specific projects via dedicated control scripts which also allow reproducible recalculation of results. Consult README file for installation and usage instructions. Further documentation is supplied in PDF format. dataMAPPs is published under GPL license by a team of scientists working for Hoffmann-La Roche AG in Basel, Switzerland. The software is provided as is, to hopefully benefit other researchers. Related Publication in the Journal of Proteome Research: DOI: 10.1021/acs.jproteome.0c00309 -
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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. -
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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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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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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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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!” -
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Ericscript is a computational framework to detect gene fusions from paired end RNA-seq data with high sensitity and specificity.
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Excelsi-R
Excel's Interface to R
Excelsi-R is an Excel add-in that allows Excel spreadsheets to harness the power of the R statistical language by connecting to a running R process. R does not need to be installed on the computer running Excel, but it does require access to an R instance running Rserve (which can be on a remote computer). It supports incorporating R plots and graphs in an Excel workbook. -
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An R package that performs forward-backward fragment assembling segmentation. Reference: BMC Bioinformatics. 2007 May 3;8:145.
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FishingCNV
Copy number variation (CNV) detection in exome sequencing data
FishingCNV, a software tool developed at McGill University, is a tool for comprehensive analysis of rare copy number variations in high-throughput exome sequencing data. The inputs are standard coverage files produced by Genome Analysis ToolKit (GATK), and the output is a file containing putative CNVs. The program comes in 2 different releases * GUI version (FishingCNV_X.X.X.zip) * Command line version (FishingCNV_X.X_pipeline) Browse through our files to find the different releases. -
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FunctSNP is an R interface to an SQL database for linking single nucleotide polymorphisms (SNPs) to functional knowledge. FunctSNP should work on any OS on which R runs.
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This package, for R environment, detects DAE peaks ratio on data gotten from LightCycler instrument.
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MANTIS R Package
Multi-locus ANTIgenic Simulator R-package for multi-strain pathogens
This repository contains the most updated code version of MANTIS, the Multi-locus ANTIgenic Simulator R-package. MANTIS is developed and maintained by the Evolutionary Ecology of Infectious Disease (EEID) research group at the Department of Zoology, University of Oxford, UK. For theoretical background please refer to 'MANTIS: an R package that simulates multilocus models of pathogen evolution' (https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0598-9 OPEN ACCESS) At the moment, MANTIS is not available on CRAN. To build a source package of MANTIS, use the script buildPackage.R To install MANTIS use 'install.packages(file.tar.gz,type="src",repo=NULL)' To test use test_V3.3_code.R Contact the main author of the publication above for further details. Have fun!
