- Jul 10, 2019
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Sergey Linev authored
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- Jul 08, 2019
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pamputt authored
Fix spelling
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- Jul 03, 2019
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Olivier Couet authored
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- Jun 26, 2019
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Guilherme Amadio authored
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pamputt authored
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- Jun 10, 2019
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Danilo Piparo authored
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- Jun 09, 2019
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Danilo Piparo authored
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- May 29, 2019
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Axel Naumann authored
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- May 28, 2019
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Guilherme Amadio authored
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Axel Naumann authored
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- May 27, 2019
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Stephan Hageboeck authored
The Johnson PDF is a transformed Gaussian distribution. An implementation with analytical integrals and generator functions is added. Also: - Test for equivalence with an LHCb reference implementation - Test numerical vs analytical integration to percent level - Test numerical mean value vs literature - Add release notes and images for doxygen
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- May 14, 2019
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Guilherme Amadio authored
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Guilherme Amadio authored
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Guilherme Amadio authored
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Axel Naumann authored
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Enric Tejedor Saavedra authored
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Enric Tejedor Saavedra authored
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- May 13, 2019
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Danilo Piparo authored
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Danilo Piparo authored
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- May 08, 2019
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Stefan Wunsch authored
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- May 07, 2019
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Guilherme Amadio authored
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- Apr 30, 2019
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Danilo Piparo authored
to prompt a warning whenever something needs to be deprecated irrespective of the R__SUGGEST_NEW_INTERFACE preprocessor variable definition.
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Stephan Hageboeck authored
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Danilo Piparo authored
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Danilo Piparo authored
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- Apr 29, 2019
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Sergey Linev authored
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Sergey Linev authored
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- Apr 21, 2019
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Danilo Piparo authored
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- Apr 17, 2019
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Sergey Linev authored
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Sergey Linev authored
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Sergey Linev authored
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Sergey Linev authored
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- Apr 16, 2019
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Vassil Vassilev authored
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- Apr 10, 2019
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Philippe Canal authored
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Danilo Piparo authored
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- Apr 09, 2019
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Vassil Vassilev authored
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Lorenzo Moneta authored
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Stephan Hageboeck authored
The RooFit release notes were slightly outdated after the deprecation macro R__SUGGEST_ALTERNATIVE had been merged.
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- Apr 08, 2019
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Vassil Vassilev authored
The new release includes some improvements in both Forward and Reverse mode: * Extend the way to specify a dependent variables. Consider function, `double f(double x, double y, double z) {...}`, `clad::differentiate(f, "z")` is equivalent to `clad::differentiate(f, 2)`. `clad::gradient(f, "x, y")` differentiates with respect to `x` and `y` but not `z`. The gradient results are stored in a `_result` parameter in the same order as `x` and `y` were specified. Namely, the result of `x` is stored in `_result[0]` and the result of `y` in `_result[1]`. If we invert the arguments specified in the string to `clad::gradient(f, "y, x")` the results will be stored inversely. * Enable recursive differentiation. * Support single- and multi-dimensional arrays -- works for arrays with constant size like `double A[] = {1, 2, 3};`, `double A[3];` or `double A[1][2][3][4];` See more at: https://github.com/vgvassilev/clad/blob/v0.5/docs/ReleaseNotes.md
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Brian Bockelman authored
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