Showing 9 open source projects for "monte carlo simulation"

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  • 1
    Statistics101 - Resampling Statistics

    Statistics101 - Resampling Statistics

    Use simulation to perform statistical analyses.

    ...Anyone who wants to learn statistics will find that the resampling approach helps in understanding statistical concepts from the simplest to the most difficult. In addition, professionals who want to use resampling, bootstrapping, or Monte Carlo simulations will find Statistics101 helpful. More information at https://statistics101.sourceforge.io/
    Downloads: 4 This Week
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  • 2
    MCPower

    MCPower

    MCPower — simple Monte Carlo power analysis for complex models

    MCPower-GUI is a desktop application that provides a graphical interface for the MCPower Monte Carlo power analysis library. It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario analysis), and Results (interactive charts, exportable tables, and auto-generated Python replication scripts). ...
    Downloads: 0 This Week
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  • 3
    UnBBayes

    UnBBayes

    Framework & GUI for Bayes Nets and other probabilistic models.

    UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy.
    Downloads: 3 This Week
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  • 4
    Blueprint XAS
    Blueprint XAS is a Matlab-based suite designed for the processing and analysis of near-edge x-ray absorption spectroscopy (XAS) data. The suite is designed primarily to assist users in exploring reasonable fit solutions while minimizing user bias.
    Downloads: 3 This Week
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  • 5
    rethinking

    rethinking

    Statistical Rethinking course and book package

    This R package accompanies Richard McElreath’s Statistical Rethinking (2nd edition), offering utilities to fit and compare Bayesian models using both MAP estimation (quap) and Hamiltonian Monte Carlo via RStan (ulam). It supports specifying models via explicit distributional assumptions, providing flexibility for advanced statistical workflows.
    Downloads: 0 This Week
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  • 6
    An R package for metrology. metRology provides classes and calculation and plotting functions for metrology applications, including measurement uncertainty estimation and (eventually) inter-laboratory metrology comparison studies.
    Downloads: 0 This Week
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  • 7

    MCPerm: Monte Carlo SNP permutation

    Monte Carlo permutation method for SNP multiple test correlation

    ...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.
    Downloads: 0 This Week
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  • 8

    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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  • 9
    ...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. 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: 1 This Week
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