Apophenia a library for scientific computing

Apophenia is an open statistical library for working with data sets and statistical models. It provides functions on the same level as those of the typical stats package such as OLS, Probit, or singular value decomposition but gives the user more flexibility to be creative in model-building. The core functions are written in C, but experience has shown them to be easy to bind to in PythonJuliaPerlRubyc. Functions for transforming models like Normal. Database querying and maintenance utilities.

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Ben Klemens

Or download a printable résumé. Keeping with the times, he has been blogging lately at this blog. Intellectual Property Boxes and the Paradox of Price Discrimination, May 2017. Valuing patents and trademarks in complex production chains, December 2015.

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This is the companion web site for. Also, the reference page. Includes links to documentation for the various libraries used in the book. On the blog side, see the first entry.

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In this blog I wish to share information about important link to some of the most useful free software and open source, books and manual of softwares for social science research, operations research, mathematics and statistics applications. Tuesday, April 26, 2011. What statistical analysis should I use? 1 IV with 2 levels. 1 IV with 2 levels.

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Apophenia a library for scientific computing

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Apophenia is an open statistical library for working with data sets and statistical models. It provides functions on the same level as those of the typical stats package such as OLS, Probit, or singular value decomposition but gives the user more flexibility to be creative in model-building. The core functions are written in C, but experience has shown them to be easy to bind to in PythonJuliaPerlRubyc. Functions for transforming models like Normal. Database querying and maintenance utilities.

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The website states the following, "Apophenia is an open statistical library for working with data sets and statistical models." I viewed that the web page also said " It provides functions on the same level as those of the typical stats package such as OLS, Probit, or singular value decomposition but gives the user more flexibility to be creative in model-building." They also said " The core functions are written in C, but experience has shown them to be easy to bind to in PythonJuliaPerlRubyc. Functions for transforming models like Normal. Database querying and maintenance utilities."

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