Open Source S/R Statistics Software for Linux
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S/R Statistics Software for Linux
View 10 business solutionsBrowse free open source S/R Statistics Software for Linux and projects below. Use the toggles on the left to filter open source S/R Statistics Software for Linux 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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TXM
Unicode XML TEI text analysis platform
TXM is a free and open-source cross-platform Unicode & XML based text analysis environment and graphical client, supporting Windows, Linux and Mac OS X. It can also be used online as a J2EE standard compliant web portal (GWT based) with access control built in. DOWNLOAD LATEST VERSION OF TXM : http://textometrie.ens-lyon.fr/spip.php?rubrique61&lang=en TXM offers a comprehensive range of analysis tools (concordances, collocate search, frequency lists, etc.) based on the powerfull CQP full text search engine (http://cwb.sourceforge.net) and a range of statistical functions (factorial analysis, classification, cooccurrency analysis, etc.) based on R packages (http://www.r-project.org). Read the scientific background at the Textométrie project web site http://textometrie.ens-lyon.fr/?lang=en. Read a full description at the TEI Tools wiki http://wiki.tei-c.org/index.php/TXM. -
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runjags
The 'runjags' R package and standalone JAGS extension module
This package provides high-level interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior. -
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BiomeNet
BAYESIAN INFERENCE OF METABOLIC DIVERGENCE AMONG MICROBIAL COMMUNITIES
Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic networks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). -
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A population-based method for DNA copy number analysis: recurrent copy number aberration indentification in multiple samples (with no need of single-sample calling). Developed for a quick analysis of high resolution and large population data.
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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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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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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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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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HPCall
Improved base-calling for homopolymer-sensitive next-gen data.
The current software contains the implementation for the 454 pyrosequencing platform. -
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Hyperspectral data analysis in R
Handling and basic analysis of hyperspectral data in R
The hsdar package contains classes and functions to manage, analyse and simulate hyperspectral data. These might be either spectrometer measurements or hyperspectral images through the interface of rgdal. -
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MCPerm: Monte Carlo SNP permutation
Monte Carlo permutation method for SNP multiple test correlation
MCPerm: A Monte Carlo permutation method for multiple test correlation in case-control association study Traditional permutation (TradPerm) test is an important non-parametric analysis method which can be treated as the gold standard for multiple testing corrections in case-control association study. However, it relies on the original single nucleotide polymorphism (SNP) genotypes and phenotypes data to perform a large number of random shuffles, and thus it is computationally intensive, especially for genome-wide association study (GWAS). To improve the calculation speed without changing the size of the TradPerm p-value, we developed a Monte Carlo permutation (MCPerm) method as an efficient alternative to TradPerm. Methods: MCPerm does not need to shuffle the original genotypes and phenotypes data. It uses Monte Carlo method, employs two-step hypergeometric distribution to generate the random number of genotypes (AA, Aa and aa) in cases and controls. -
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OpenM++
OpenM++: open source microsimulation platform
IMPORTANT: ========== Our project home page: https://openmpp.org Our latest source code and release available at: https://github.com/openmpp/main/releases/latest Our documentation available at: https://github.com/openmpp/openmpp.github.io/wiki SourceForge files still exist for historical reason. OpenM++ is an open source microsimulation platform inspired by and compatible with Modgen. OpenM++, compared to its closed source predecessor Modgen, has advantages like portability, scalability and open source. It is not a copy of Modgen, but a new, functionally equivalent implementation of the publicly available language specification. -
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##### The RKWard project has moved! ##### Find the most recent news and downloads at http://rkward.kde.org . RKWard aims to provide an easily extensible, easy to use IDE/GUI for R. RKWard tries to combine the power of the R-language with the (relative) ease of use of commercial statistics tools.
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SWire4R
An R package enabling R to interact with Stata for data exchange
Swire4R is an R package for exchanging data between R and Stata. SWire4R uses SWire for connecting R to Stata. SWire is a plugin for Stata >=13 which acts like a server: client applications can query SWire for interacting with Stata. SWire4R acts like a client application for Swire, providing the user with various basic functions for retrieving data from Stata and exporting data to Stata. SWire4R must be compiled by R. For more information about SWire, visit https://sourceforge.net/projects/swire/ -
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Opensource software projects for genetics and genomics from Sage Bionetworks.
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TOPS_CeMM
User Friendly Data Analysis Tool for Interaction Data
TOPS provides the benchtop scientist with a free toolset to analyze, filter and visualize data from functional genomic gene-gene and gene-drug interaction screens with a flexible interface to accommodate various different technologies and analysis algorithms in addition to those already provided here. -
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Yet Another Data Mining Platform
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Weibull-based reliability toolkit for R
R package for Weibull analysis on (life-)time observations.
This is a small R package for doing Weibull-based reliability analysis. This R package is now obsolete and has been superseded by 'project Abernethy' on http://r-forge.r-project.org/projects/abernethy/. -
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An R package implementation of a consensus clustering methodology. This package allows users to perform re-sampling statistics based clustering using multiple clustering algorithms to assess the robustness of both clusters and members of clusters.
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hSDM
R package for hierarchical species distribution models
hSDM is an R package for hierarchical species distribution models. Such models allows interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. -
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