Showing 38 open source projects for "using"

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  • 1
    phcfM

    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.
    Downloads: 0 This Week
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  • 2

    GENIE (GEne-geNe IntEraction)

    GPU based Parallel Gene-Gene Interaction Analysis

    ...Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. Citation: Chikkagoudar, S., Wang, K., & Li, M. (2011). GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores. BMC research notes, 4(1), 158.
    Downloads: 0 This Week
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  • 3

    StochDecomp

    User Manual describing theory behind the package, installation instruc

    ...Here we present a flexible and generally applicable noise decomposition tool, that allows us to calculate contributions of individual reactions to the total variability of a system’s output. With the package it is therefore possible to quantify how the noise enters and propagates in biochemical systems. We also demonstrate and exemplify using the JAK-STAT signalling pathway that it is possible to infer noise contributions resulting from individual reactions directly from experimental data. This is the first computational tool that allows to decompose noise into contributions resulting from individual reactions.
    Downloads: 0 This Week
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  • 4
    DeDAY

    DeDAY

    MLE survival analysis: Gompertz, Weibull, Logistic and mixed morality.

    ...Mixed models partition mortality into exogenous and endogenous components, so that the intrinsic survivorship can be estimated without the interference from extrinsic noise. DeDAY supports both interval-censored data and exact event-time data. Using MLE (Maximum Likelihood Estimate), DeDAY fits statistic model to the data. DeDAY also calculates the variances and the multi-dimensional confidence limits of model parameters. DeDAY is free for academic users.
    Downloads: 0 This Week
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  • 5

    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/.
    Downloads: 0 This Week
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  • 6

    Math Simulator

    A simulation software for numerical techniques.

    Math Simulator is a java-based application which simulates various mathematical techniques.It is extremely useful for the Engineering students and for professionals. Most importantly, unlike most others Math Simulator is an Application rather than a java - library. So, user is not required to have any computer background. A Unique feature of Math Simulator is that it allows you to save queries for later reference. Hence you can save the problems along with their results.
    Downloads: 0 This Week
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  • 7

    DyMMM

    Dynamic Multispecies Metabolic Modeling framework

    ...Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. The ISME journal. Zhuang, K., Ma, E., Lovley, D. R., & Mahadevan, R. (2012). The design of long-term effective uranium bioremediation strategy using a community metabolic model. Biotechnology and Bioengineering.
    Downloads: 0 This Week
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  • 8
    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.
    Downloads: 10 This Week
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  • 9
    FastPval is multiple stage p-value computing software that computes empirical p-values from a large set of permutated/resampled background data.
    Downloads: 0 This Week
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  • 10
    clusterCons
    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.
    Downloads: 0 This Week
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  • 11
    This project aims to provide open source software implementing the Wedge algorithm for the estimation of parameters in dynamical systems models. This project also seeks to create tutorials and resources for those using Wedge.
    Downloads: 0 This Week
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  • 12
    ...The considered implemented parametric and non-parametric Monte Carlo likelihood methods were chosen for their ability to treat both one-dimensional and multivariate SDE systems, although the quality of the inferential results can't match those obtained using more advanced techniques. Nevertheless the toolbox capabilities to simulate numerical solutions of SDE systems are still valid and can serve as a useful starting point to those willing to simulate stochastic dynamical models easily.
    Downloads: 5 This Week
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  • 13

    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.
    Downloads: 0 This Week
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