Showing 5 open source projects for "parameter estimation"

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  • 1
    statsmodels

    statsmodels

    Statsmodels, statistical modeling and econometrics in Python

    statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license.
    Downloads: 0 This Week
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  • 2
    DEBay

    DEBay

    Deconvolutes qPCR data to estimate cell-type-specific gene expression

    DEBay: Deconvolution of Ensemble through Bayes-approach DEBay estimates cell type-specific gene expression by deconvolution of quantitative PCR data of a mixed population. It will be useful in experiments where the segregation of different cell types in a sample is arduous, but the proportion of different cell types in the sample can be measured. DEBay uses the population distribution data and the qPCR data to calculate the relative expression of the target gene in different cell types in...
    Downloads: 4 This Week
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  • 3

    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 This R package is now obsolete and has been superseded by 'project Abernethy' on http://r-forge.r-project.org/projects/abernethy/.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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  • 4
    The Automated Parameter Estimation and Model Selection Toolkit is a fast, parallelized MCMC engine written in C for Bayesian inference (parameter estimation and model selection).
    Downloads: 0 This Week
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  • 5
    ...A note of caution: SDE Toolbox is no more developed but it's still downloadable. Its inferential capabilities can be considered surpassed (at best). Actually the parameter estimation methods were already far from the state-of-art when the project began in 2007 (!). 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. ...
    Downloads: 3 This Week
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