Search Results for "statistical" - Page 2
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Showing 49 open source projects for "statistical"
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PMM-Lab
Predictive Microbial Modeling plug-in for KNIME
...It consists of three components: • a library of KNIME nodes (called PMM-Lab), • a library of “standard” workflows • an HSQL database.to store experimental data and microbial models. Altogether these components are designed to ease and standardize the statistical analysis of experimental microbial data and the development of predictive microbial models (PMM). Users can apply PMM-Lab to proprietary or public data and create bacterial growth / survival / inactivation models. The framework can easily be extended to other model types, e.g. growth/no-growth boundary models. PMM-Lab has been initiated and provided by the Federal Institute for Risk Assessment - BfR (Berlin, Germany). ... -
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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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Adaptive Gaussian Filtering
Machine learning with Gaussian kernels.
Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation routines. ... -
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Chordalysis
Log-linear analysis (data modelling) for high-dimensional data
===== Project moved to https://github.com/fpetitjean/Chordalysis ===== Log-linear analysis is the statistical method used to capture multi-way relationships between variables. However, due to its exponential nature, previous approaches did not allow scale-up to more than a dozen variables. We present here Chordalysis, a log-linear analysis method for big data. Chordalysis exploits recent discoveries in graph theory by representing complex models as compositions of triangular structures, also known as chordal graphs. ... -
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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. ... -
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SWP Project
SWP is for Weighted Points
SWP is a script language to manipulate sets of isolated weighted point, mainly analysis of fractal and other statistical properties of such sets. Originally started as a tool to explore fractal properties of cosmic fractal like large-scale distribution of galaxies. Could be used for any similar sets either. -
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PhEq_bootstrap
FDA's f2 computation with bootstrap technique
This program was developed as a help in establishing pharmaceutical equivalence by use of FDA f2 coefficient. It was designed to help with f2 computation in cases when intra- and inter-batch variability is large, namely RSD>10%. The use of statistical bootstrap technique allows to implement confidence interval (CI) into the f2 coefficients resulting in overcoming of their major drawback in the original metrics. The algorithm provides possible “worst case scenario” of f2 values, thus supporting claim about pharmaceutical equivalence. The target users are researchers from industry and academia dealing with pharmaceutical equivalence problem. ... -
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SalStat is a small application for statistical analysis emphasising the sciences and social sciences (particularly Psychology). The project is designed around the user interface which has been designed to be simple to use. Think SPSS, but better!
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phcfM
R package for modelling anthropogenic deforestation
...It was named after the REDD+ pilot-project 'programme holistique de conservation des forêts à Madagascar'. phcfM includes two main functions: (i) demography(), to model the population growth with time in a hierarchical Bayesian framework using population census data and Gaussian linear mixed models and (ii) deforestation(), to model the deforestation process in a hierarchical Bayesian framework using land-cover change data and Binomial logistic regression models with variable time-intervals between land-cover observations. The two functions use embedded Gibbs samplers written in C++ with the Scythe statistical library to reduce computational time. -
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Implementation in Python of some of the statistical methods provided by "asurv", the survival analysis software.
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Soothsayer
Capacity exhaustion prediction from RRD files
Predict if storage utilised will reach capacity within defined period (maximum 5 years), using recent historical data contained within RRD files (maximum -1 year). Produces HTML (or partial HTML) and, more importanly, a PNG RRD Graph showing the prediction and, should the capacity be reached, an annotation indicating when. Optionally email out exception notices. -
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EnvStats
An R Package for Environmental Statistics
...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. ... -
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Please participate in the SURVEY on rgedit's future: https://www.surveymonkey.com/s/VNMMJMJ your answers are much appreciated! Gedit (Gnome editor, www.gedit.org) plug-in allowing it to become an easy-to-use and yet light-weight IDE for the statistical programming environment, R (www.r-project.org).
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FreDec
Parallelized FREquency DEComposer algorithm
...The algorithm analyses all suspicious frequencies, including the ones that look like an alias at a glance, but may become preferable later. After selection of the initial frequency candidates, the algorithm passes through all their possible combinations and estimates their multi-frequency statistical significance. In the end, it prints out the set of largest frequency tuples that were still found significant. The GPU computing is implemented through CUDA and brings a significant performance increase. It is still possible to run FreDec solely on CPU, if no suitable GPU device is available in the system. See the details of the underlying theory in Baluev 2013, MNRAS, V. 436, P. 807 The description of the algorithm itself can be found in arXiv:1309.0100. ... -
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Myrtle
A simple programmable spreadsheet for learning statistics.
Myrtle is a simple programmable spreadsheet and statistical analysis software specifically designed for learning statistics. It provides the standard spreadsheet functionality one would expect like multiple tabbed sheets, relative and absolute row and column referencing in formulas, and a large catalog of built-in functions. Functions specific to logic and computer science, mathematics, probability, and statistics are available. -
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r2web
Software for medical statistics based on R
The open source software R enables a large number of statistical analyzes. The software R2web allows to use those R functions that are useful for medical students, with a web interface that facilitates the work of the student (it replaces line commands in R with checkboxes, radio buttons, etc. ). Un logiciel en ligne pour les statistiques pour la médecine, basé sur R. Le logiciel open source R permet de faire un grand nombre d'analyses statistiques. -
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Statistical models with python using numpy and scipy. Currently covers linear regression (with ordinary, generalized and weighted least squares), robust linear regression, and generalized linear model, discrete models, time series analysis and other statistical methods.
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M-S Lab
Math-Statistics LABoratory
M-S Lab is a free software for statistical computing and graphics. M-S Lab is available in two distributions: The built-in language support is implemented in "Own" version. And for users who like to code in Lua scripting language we made a "Lua" version. Both versions are available for free download in the Downloads page. -
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libcomposition
A C library to handle compositional (=closed) data, like proportions.
Inspired by Aitchison 2003 (http://www.amazon.com/The-Statistical-Analysis-Compositional-Data/dp/1930665784/) this library aims to make compositional data analysis rigorously. -
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This project adds a ribbon to excel with a number of statistical buttons. This is not intended to be a full statistical analysis package (like R), but only a source for quick visualizations and calculations. For Excel2007 and up
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MooGraph is an utility to produce interactive dynamical graphs from statistical data. Deliberately inspired to GapMinder, it represents the evolution of multivariate data in time by means of coloured bubbles of variable sizes on an (x,y) plot.
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This project hosts tools used for analysis of Gaussian Mixture Distributions (GMDs) which are used for statistical signal processing. The tools are libraries for implementing GMD operations and programs used to analyze properties of GMDs.
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Statastizard
Statistics Wizard
Statastizard(c)2012 Nick Emblow is a program designed to guide its user into the right statistical test, perform the test, and report the results in a meaningful way. The idea of this is that the user won't actually need to know that they are using a mann-whitney U over a t-test, for example - it just tests for normality and homoscedastic of variance, and decides which test to use, then reports the results. Why? Because time after time, people come to me with some test that they've done that they think "proves" something - when in reality they've either not done a comparative test to start with (ex. a correlation), failed to test for confounding factors (such as normality etc) and as a result, failed to appropriately identify even the right test to do.
