Flavio

This minor release fixes two bugs,

- a bug in the newly released frequentist fit class that erroneously included constraints on fit parameters in the likelihood (rather than ignoring them),
- interchanged B⁰ matrix elements for non-SM operators (thanks to Zeren Simon Wang for reporting & fixing it). The numerical impact of this bug was (accidentally) tiny.

New feature: frequentist fits & profile likelihoods

The major new feature in the release is the addition of **frequentist fits** to complement the Bayesian fits that have existed ever since v0.5. This allows to do analyses with both Bayesian and frequentist statistics in a single framework.

To determine frequentist confidence levels in one or two dimensions, there is a new **profile likelihood fitter**. In two dimensions, the results can be visualized using the existing `contour` plot function, while for one-dimensional p-value distributions, there is a **new plot function** `pvalue_plot` (which was inspired by the pretty plots generated by the [GammaCombo](https://gammacombo.github.io/) package).

On this occasion, the documentation of fits & fitters has been overhauled completely. See in particular the new [introduction to inference in flavio](https://flav-io.github.io/docs/fits.html), the extended [discussion of Bayesian fits](https://flav-io.github.io/docs/bayesian.html), and the new [discussion of frequentist fits](https://flav-io.github.io/docs/frequentist.html).

There is also a [new notebook](https://github.com/flav-io/flavio-examples/blob/master/FrequentistFit_VubVcb_Profiler.ipynb) in the flavio examples repository demonstrating the use of the new fitter and plots.

Other improvements

- A major limitation of the `sm_error_budget` function and its visualization `error_budget_pie` to display error budgets of SM predictions has been removed: previously, the error budget contributions were obtained by individually varying each parameter one by one; this lead to misleading results in case some of the parameters are strongly correlated. This applies for instance to angular observables in B→Vll decays, where uncertainties partially drop out in ratios. The new version now groups together all correlated parameters into common slices of the pie.
- The top quark mass dependence in the matching condition of the Wilson coefficient C₁₀ is now
approximately taken into account (previously, it was taken as a constant).

Bug fixes

- missing conjugation in QCDF corrections (thanks Jacky Kumar jackypheno)
- wrong warning when defining several fits (thanks Peter Stangl peterstangl for reporting & fixing it)

This minor release brings two small improvements and corresponds to the numerics used in the published version of [arXiv:1703.09189](https://arxiv.org/abs/1703.09189).

Improvements

- Correlations between LHCb measurements of B_s→φll angular observables added (thanks to Javier Virto for suggesting and to peterstangl for implementing it).
- In the [last release](https://github.com/flav-io/flavio/releases/tag/v0.21.1), the integration precision for theoretically clean lepton flavour universality tests was increased; this was now implemented also for B_s→φll.

This minor release fixes two problems:

- It increases the numerical integration precision for observables that have a very small SM uncertainty (like the lepton flavour universality ratios in B decays), where this precision is relevant
- It corrects the renormalization scale used for the computation of the SM contribution to meson-antimeson mixing, which previously was not consistent with the QCD correction factors used (thanks to Jacky Kumar for pointing out this problem)

This point release fixes a few minor issues and adds or updates the following experimental measurements:

- Missing observables in the 2015 CMS [measurement](https://arxiv.org/abs/1507.08126) of B→K*μ⁺μ⁻
- 2013 CMS [measurement](https://arxiv.org/abs/1308.3409) of B→K*μ⁺μ⁻ (which is statistically independent of the former)
- 2017 LHCb measurement of B_s→μ⁺μ⁻ updated to [published version](https://arxiv.org/abs/1703.05747). Thanks to Marc Olivier Bettler and Flavio Archilli for useful correspondence and for providing the 2D likelihood data.