Removes lsqfit.BayesIntegrator (and BayesPDF) which is replaced by the more capable vegas.PDFIntegrator in the vegas module (for multi-dimensional integration). Adds two new methods to nonlinear_fit, for use with PDFIntegrator: logpdf(p) and pdf(p). Fixes for uncommon bugs when maxit=0 or when using the noise parameter with no prior
Very minor changes: bug fix and small new feature in Multifitter; documentation fixes
Adds new keywords add_svdnoise and add_priornoise to help assess fit quality; see new sections on Go...
Adds new class lsqfit.MultiFitter. This provides a framework for organizing complex fits around a sm...
Introduces improved pickling which means that multiple processors (nproc>1) can be used when doing B...
Removes the extend parameter from lsqfit.nonlinear_fit. That parameter is no longer necessary: the a...
Adds a new keyword linear to lsqfit.nonlinear_fit(...) that lists (optionally) which fit parameters ...
<p>These Python packages are for fitting noisy data by multi-dimensional, nonlinear functions of arb...
Bug fix in lsqfit.wavg(...) for problem that was having an adverse effect on certain of lsqfit.Multi...
New, much improved implementation of vegas.PDFIntegrator. It is slightly incompatible with the previ...
New class BayesIntegrator for evaluating Bayesian integrals. New parameter distributions
This version offers a major overhaul of the underlying fitting strategies used by lsqfit, featuring ...
Fixes a bug in empbayes_fit(z0, ...) that occurred when z0 was an integer or contained integers. Als...
Minor update: Setting p0=True causes nonlinear_fit() to choose the fit search's starting point at ra...
This provides: a minor bug fix in lsqfit.MultiFitter.chained_fit concerning fit.svdcorrection; and a...
Small improvement in how p0 is handled when using MultiFitter -- responds to a feature request
Very minor changes: bug fix and small new feature in Multifitter; documentation fixes
Adds new keywords add_svdnoise and add_priornoise to help assess fit quality; see new sections on Go...
Adds new class lsqfit.MultiFitter. This provides a framework for organizing complex fits around a sm...
Introduces improved pickling which means that multiple processors (nproc>1) can be used when doing B...
Removes the extend parameter from lsqfit.nonlinear_fit. That parameter is no longer necessary: the a...
Adds a new keyword linear to lsqfit.nonlinear_fit(...) that lists (optionally) which fit parameters ...
<p>These Python packages are for fitting noisy data by multi-dimensional, nonlinear functions of arb...
Bug fix in lsqfit.wavg(...) for problem that was having an adverse effect on certain of lsqfit.Multi...
New, much improved implementation of vegas.PDFIntegrator. It is slightly incompatible with the previ...
New class BayesIntegrator for evaluating Bayesian integrals. New parameter distributions
This version offers a major overhaul of the underlying fitting strategies used by lsqfit, featuring ...
Fixes a bug in empbayes_fit(z0, ...) that occurred when z0 was an integer or contained integers. Als...
Minor update: Setting p0=True causes nonlinear_fit() to choose the fit search's starting point at ra...
This provides: a minor bug fix in lsqfit.MultiFitter.chained_fit concerning fit.svdcorrection; and a...
Small improvement in how p0 is handled when using MultiFitter -- responds to a feature request
Very minor changes: bug fix and small new feature in Multifitter; documentation fixes
Adds new keywords add_svdnoise and add_priornoise to help assess fit quality; see new sections on Go...
Adds new class lsqfit.MultiFitter. This provides a framework for organizing complex fits around a sm...
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