bsci | statistical bootstrap to construct confidence intervals

 by   famura Python Version: 1.1 License: MIT

kandi X-RAY | bsci Summary

kandi X-RAY | bsci Summary

bsci is a Python library. bsci has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install bsci' or download it from GitHub, PyPI.

A frequentist approach to construct confidence intervals by sampling with replacement. The statistical boostrap method was first introduced by B. Efron, "Bootstrap methods: another look at the jackknife", Annals of Statistics, 1979, link to pdf. These lecture notes provide a nice entry-level introduction. More references are given in the code.
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            kandi-support Support

              bsci has a low active ecosystem.
              It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              bsci has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of bsci is 1.1

            kandi-Quality Quality

              bsci has no bugs reported.

            kandi-Security Security

              bsci has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              bsci is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              bsci releases are not available. You will need to build from source code and install.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

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            bsci Key Features

            No Key Features are available at this moment for bsci.

            bsci Examples and Code Snippets

            No Code Snippets are available at this moment for bsci.

            Community Discussions

            No Community Discussions are available at this moment for bsci.Refer to stack overflow page for discussions.

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install bsci

            To install the core part of the package run. For (local) development install the dependencies with.
            Play around with the model's parameters in the demo.py script.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            Install
          • PyPI

            pip install bsci

          • CLONE
          • HTTPS

            https://github.com/famura/bsci.git

          • CLI

            gh repo clone famura/bsci

          • sshUrl

            git@github.com:famura/bsci.git

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