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One of the tests exceeded the number of iterations when using the
d_3 error model algorithm, so only use "direct" in this case.
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The new algorithm tries direct optimization of the likelihood, as well
as a three step procedure. In this way, we consistently get the
model with the highest likelihood for SFO, DFOP and HS for all 12
new test datasets.
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by plotting squared residuals against predicted values, and
showing the variance function used in the fitted error model.
Rebuild docs
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Static documentation rebuilt by pkgdown
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Now we have a three stage fitting process for
nonconstant error models:
- Unweighted least squares
- Only optimize the error model
- Optimize both
Static documentation rebuilt by pkgdown
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- Also make it possible to specify initial values for error model
parameters.
- Run tests
- Rebuild docs
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Remove skipped tests as I do not intend to reactivate them
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- Write the NEWS
- Static documentation rebuilt by pkgdown
- Adapt mkinerrmin
- Fix (hopefully all) remaining problems in mkinfit
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Static documentation rebuilt by pkgdown
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Also test the model specification via the link argument
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