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These packages facilitate least-squares fitting of noisy data by multi-dimensional, nonlinear functions of arbitrarily many parameters. The central package is :mod:`lsqfit` which provides the fitting capability. :mod:`lsqfit` makes heavy use of package :mod:`gvar`, which provides tools for the analysis of error propagation, and also for the creation of complicated multi-dimensional gaussian distributions. :mod:`lsqfit` supports Bayesian priors for the fit parameters, with arbitrarily complicated multidimensional gaussian distributions. It uses automatic differentiation to compute gradients, greatly simplifying the design of fit functions.
19 lines
637 B
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19 lines
637 B
Text
@comment $NetBSD: PLIST,v 1.1 2013/08/31 20:23:39 jihbed Exp $
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${PYSITELIB}/gvar/__init__.py
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${PYSITELIB}/gvar/__init__.pyc
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${PYSITELIB}/gvar/__init__.pyo
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${PYSITELIB}/gvar/_bufferdict.so
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${PYSITELIB}/gvar/_gvarcore.so
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${PYSITELIB}/gvar/_svec_smat.so
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${PYSITELIB}/gvar/_utilities.so
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${PYSITELIB}/gvar/dataset.so
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${PYSITELIB}/lsqfit/__init__.py
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${PYSITELIB}/lsqfit/__init__.pyc
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${PYSITELIB}/lsqfit/__init__.pyo
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${PYSITELIB}/lsqfit/_extras.py
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${PYSITELIB}/lsqfit/_extras.pyc
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${PYSITELIB}/lsqfit/_extras.pyo
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${PYSITELIB}/lsqfit/_utilities.so
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${PYSITELIB}/lsqfit/_version.py
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${PYSITELIB}/lsqfit/_version.pyc
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${PYSITELIB}/lsqfit/_version.pyo
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