[RF][RelNotes] Mention removal of const char* to RooCmdArg conversion

RooFit got more strict and forbids the implicit construction of
meaningless RooCmdArgs from strings. Our users should be warned about
possible new compiler errors if they make this mistake.

README/ReleaseNotes/v626/index.md

@@ -232,14 +232,14 @@ For benchmarking results on the prototype version of the parallelized gradient c
 
 Various new pythonizations are introduced to streamline your RooFit code in Python.
 
-For a complete list of all pythonized classes and functions, please see the [RooFit pythonizations page in the reference guide](https://root.cern.ch/doc/v626/group__RoofitPythonizations.html).
+For a complete list of all pythonized classes and functions, please see the [RooFit pythonizations page in the reference guide](https://root.cern/doc/v626/group__RoofitPythonizations.html).
 All RooFit Python tutorials have been updated to profit from all available pythonizations.
 
 Some notable highlights are listed in the following.
 
 #### Keyword argument pythonizations
 
-All functions that take RooFit command arguments as parameters now accept equivalent Python keyword arguments, for example simplifying calls to [RooAbsPdf::fitTo()](https://root.cern.ch/doc/v626/classRooAbsPdf.html#a5f79f16f4a26a19c9e66fb5c080f59c5) such as:
+All functions that take RooFit command arguments as parameters now accept equivalent Python keyword arguments, for example simplifying calls to [RooAbsPdf::fitTo()](https://root.cern/doc/v626/classRooAbsPdf.html#a5f79f16f4a26a19c9e66fb5c080f59c5) such as:
 ```Python
 model.fitTo(data, ROOT.RooFit.Range("left,right"), ROOT.RooFit.Save())
 ```
@@ -261,34 +261,34 @@ Combining the enum pythonization with the keyword argument pythonization explain
 data.plotOn(frame, DataError="SumW2")
 ```
 
-This pythonization is also useful for your calls to [RooFit::LineColor()](https://root.cern.ch/doc/v626/group__Plotting.html#gad309cf5f63ec87ae5a7025d530f0398f) or [RooFit::LineStyle](https://root.cern.ch/doc/v626/group__Plotting.html#gaf1f7922ba5965c1a5a9791a00ef354cb), to give some more common examples.
+This pythonization is also useful for your calls to [RooFit::LineColor()](https://root.cern/doc/v626/group__Plotting.html#gad309cf5f63ec87ae5a7025d530f0398f) or [RooFit::LineStyle](https://root.cern/doc/v626/group__Plotting.html#gaf1f7922ba5965c1a5a9791a00ef354cb), to give some more common examples.
 
 #### Implicit conversion from Python collections to RooFit collections
 
 You can now benefit from implicit conversion from Python lists to RooArgLists, and from Python sets to RooArgSets.
 
-For example, you can call [RooAbsPdf::generate()](https://root.cern.ch/doc/v626/classRooAbsPdf.html#a87926e1044acf4403d8d5f1d366f6591) with a Python set to specify the observables:
+For example, you can call [RooAbsPdf::generate()](https://root.cern/doc/v626/classRooAbsPdf.html#a87926e1044acf4403d8d5f1d366f6591) with a Python set to specify the observables:
 ```Python
 pdf.generate({x, y, cut}, 10000)
 ```
 
-Or, you can create a [RooPolynomial](https://root.cern.ch/doc/v626/classRooPolynomial.html) from a Python list of coefficients:
+Or, you can create a [RooPolynomial](https://root.cern/doc/v626/classRooPolynomial.html) from a Python list of coefficients:
 ```Python
 ROOT.RooPolynomial("p", "p", x, [0.01, -0.01, 0.0004])
 ```
 
-Note that here we benefit from another new feature: the implicit call to [RooFit::RooConst()](https://root.cern.ch/doc/v626/group__CmdArgs.html#gaabf71812817894196e743cf2ef1d1e7b) when passing raw numbers to the RooFit collection constructors.
+Note that here we benefit from another new feature: the implicit call to [RooFit::RooConst()](https://root.cern/doc/v626/group__CmdArgs.html#gaabf71812817894196e743cf2ef1d1e7b) when passing raw numbers to the RooFit collection constructors.
 
 #### Allow for use of Python collections instead of C++ STL containers
 
-Some RooFit functions take STL map-like types such as `std::map` as parameters, for example the [RooCategory constructor](https://root.cern.ch/doc/v626/classRooCategory.html#ae63ae78231765d184b7a839c74746a49). In the past, you had to create the correct C++ class in Python, but now you can usually pass a Python dictionary instead. For example, a RooCategory can be created like this:
+Some RooFit functions take STL map-like types such as `std::map` as parameters, for example the [RooCategory constructor](https://root.cern/doc/v626/classRooCategory.html#ae63ae78231765d184b7a839c74746a49). In the past, you had to create the correct C++ class in Python, but now you can usually pass a Python dictionary instead. For example, a RooCategory can be created like this:
 ```Python
 sample = ROOT.RooCategory("sample", "sample", {"Sample1": 1, "Sample2": 2, "Sample3": 3})
 ```
 
 #### RooWorkspace accessors
 
-In Python, you can now get objects stored in a [RooWorkspace](https://root.cern.ch/doc/v626/classRooWorkspace.html) with the item retrieval operator, and the return value is also always downcasted to the correcy type. That means in Python you don't have to use [RooWorkspace::var()](https://root.cern.ch/doc/v626/classRooWorkspace.html#acf5f9126ee264c234721a4ed1f9bf837) to access variables or [RooWorkspace::pdf()](https://root.cern.ch/doc/v626/classRooWorkspace.html#afa7384cece424a1a94a644bb05549eee) to access pdfs, but you can always get any object using square brackets. For example:
+In Python, you can now get objects stored in a [RooWorkspace](https://root.cern/doc/v626/classRooWorkspace.html) with the item retrieval operator, and the return value is also always downcasted to the correcy type. That means in Python you don't have to use [RooWorkspace::var()](https://root.cern/doc/v626/classRooWorkspace.html#acf5f9126ee264c234721a4ed1f9bf837) to access variables or [RooWorkspace::pdf()](https://root.cern/doc/v626/classRooWorkspace.html#afa7384cece424a1a94a644bb05549eee) to access pdfs, but you can always get any object using square brackets. For example:
 ```Python
 # w is a RooWorkspace instance that contains the variables `x`, `y`, and `z` for which we want to generate toy data:
 model.generate({w["x"], w["y"], w["z"]}, 1000)
@@ -310,14 +310,14 @@ For more details, consult the tutorial [rf409_NumPyPandasToRooFit.py](https://ro
 
 ### Modeling Effective Field Theory distributions with RooLadgrangianMorphFunc
 
-The [**RooLagrangianMorphFunc**](https://root.cern.ch/doc/v626/classRooLagrangianMorphFunc.html) class is a new RooFit class for modeling a continuous distribution of an observable as a function of the parameters of an effective field theory given the distribution sampled at some points in the parameter space.
+The [**RooLagrangianMorphFunc**](https://root.cern/doc/v626/classRooLagrangianMorphFunc.html) class is a new RooFit class for modeling a continuous distribution of an observable as a function of the parameters of an effective field theory given the distribution sampled at some points in the parameter space.
 Two new classes to help to provide this functionality:
   * [RooRatio](https://root.cern/doc/v626/classRooRatio.html): computes the ratio of RooFit objects 
   * [RooPolyFunc](https://root.cern/doc/v626/classRooPolyFunc.html): multi-dimensional polynomial function, were [RooPolyFunc::taylorExpand()](https://root.cern/doc/v626/classRooPolyFunc.html#a21c6671e1b2391d08dbc62c5a5e7be38) can be used to obtain the (multi-dimensional) Taylor expansion up to the second order
 
-For example usage of the RooLagrangianMorphFunc class, please consult the tutorials for a single parameter case ([rf711_lagrangianmorph.C](https://root.cern.ch/doc/v626/rf711__lagrangianmorph_8C.html) / [.py](https://root.cern.ch/doc/v626/rf711__lagrangianmorph_8py.html)) and for a multi-parameter case ([rf712_lagrangianmorphfit.C](https://root.cern.ch/doc/v626/rf712__lagrangianmorphfit_8C.html) / [.py](https://root.cern.ch/doc/v626/rf712__lagrangianmorphfit_8py.html)).
+For example usage of the RooLagrangianMorphFunc class, please consult the tutorials for a single parameter case ([rf711_lagrangianmorph.C](https://root.cern/doc/v626/rf711__lagrangianmorph_8C.html) / [.py](https://root.cern/doc/v626/rf711__lagrangianmorph_8py.html)) and for a multi-parameter case ([rf712_lagrangianmorphfit.C](https://root.cern/doc/v626/rf712__lagrangianmorphfit_8C.html) / [.py](https://root.cern/doc/v626/rf712__lagrangianmorphfit_8py.html)).
 
-A `RooLagrangianMorphFunc` can also be created with the `RooWorkspace::factory` interface, showcased in [rf512_wsfactory_oper.C](https://root.cern.ch/doc/v626/rf512__wsfactory__oper_8C.html) / [.py](https://root.cern.ch/doc/master/rf512__wsfactory__oper_8py.html).
+A `RooLagrangianMorphFunc` can also be created with the `RooWorkspace::factory` interface, showcased in [rf512_wsfactory_oper.C](https://root.cern/doc/v626/rf512__wsfactory__oper_8C.html) / [.py](https://root.cern/doc/master/rf512__wsfactory__oper_8py.html).
 
 ### Exporting and importing `RooWorkspace` to and from JSON and YML
 
@@ -392,7 +392,7 @@ To access the set of global observables stored in a `RooAbsData`, call `RooAbsDa
 It returns a `nullptr` if no global observable snapshots are stored in the dataset.
 
 For more information of global observables and how to attach them to the toy datasets, please take a look at the new
-[rf613_global_observables.C](https://root.cern.ch/doc/v626/rf613__global_observables_8C.html) / [.py](https://root.cern.ch/doc/v626/rf613__global_observables_8py.html) tutorial.
+[rf613_global_observables.C](https://root.cern/doc/v626/rf613__global_observables_8C.html) / [.py](https://root.cern/doc/v626/rf613__global_observables_8py.html) tutorial.
 
 ### Changes in `RooAbsPdf::fitTo` behaviour for multi-range fits
 
@@ -413,6 +413,21 @@ Before 6.26, it was possible to still use the `RooMinuit` by passing the `Minimi
 The class version of `RooAbsArg` was incremented from 7 to 8 in this release. In some circumstances, this can cause warnings in `TStreamerInfo` for classes inheriting from `RooAbsArg` when reading
 older RooFit models from a file. These warnings are harmless and can be avoided by incrementing also the class version of the inheriting class.
 
+### Compile-time protection against creating empty `RooCmdArg`s from strings
+
+The implicit [RooCmdArg](https://root.cern/doc/v626/classRooCmdArg.html) constructor from `const char*` was removed to avoid the accidental construction of meaningless RooCmdArgs that only have a name but no payload.
+This causes new compiler errors in your code if you pass a string instead of a RooCmdArg to various RooFit functions, such as [RooAbsPdf::fitTo()](https://root.cern/doc/v626/classRooAbsPdf.html#a5f79f16f4a26a19c9e66fb5c080f59c5).
+If this happens, please consult the documentation of [fitTo()](https://root.cern/doc/v626/classRooAbsPdf.html#a5f79f16f4a26a19c9e66fb5c080f59c5) to check which of the [free functions in the `RooFit` namespace](https://root.cern/doc/v626/group__CmdArgs.html) you need to use to achieve the desired configuration.
+
+**Example** of an error that is now caught at compile time: confusing the [RooAbsPdf::fitTo()]() function signature with the one of [TH1::Fit()](https://root.cern/doc/v626/classTH1.html#a63eb028df86bc86c8e20c989eb23fb2a) and passing the fit range name as a string literal:
+
+```C++
+pdf.fitTo(*data, "r"); // ERROR!
+// Will not compile anymore, as `"r"` is not a recognized command and will be ignored!
+// Instead, to restrict to a range called "r", use:
+pdf.fitTo(*data, RooFit::Range("r"));
+```
+
 ## TMVA
 
 ### SOFIE : Code generation for fast inference of Deep Learning models
@@ -431,7 +446,7 @@ model.Generate();
 model.OutputGenerated(“./example_output.hxx”);
 ```
 And an C++ header file will be generated. In addition also a text file, `example_output.dat` will be also generated. This file will contain the model weight values that will be used to initialize the model.
-A full example for parsing an ONNX input file is given by the tutorial [`TMVA_SOFIE_ONNX.C`](https://root.cern.ch/doc/master/TMVA__SOFIE__ONNX_8C.html). 
+A full example for parsing an ONNX input file is given by the tutorial [`TMVA_SOFIE_ONNX.C`](https://root.cern/doc/master/TMVA__SOFIE__ONNX_8C.html). 
 
 To use the generated inference code, you need to create a `Session` class and call the function `Session::inder(float *)`:
 
@@ -451,13 +466,13 @@ For parsing a Keras input file you need to do:
 ```
 SOFIE::RModel model = SOFIE::PyKeras::Parse("KerasModel.h5");
 ```
-See the tutorial [`TMVA_SOFIE_Keras.C`](https://root.cern.ch/doc/master/TMVA__SOFIE__Keras_8C.html).
+See the tutorial [`TMVA_SOFIE_Keras.C`](https://root.cern/doc/master/TMVA__SOFIE__Keras_8C.html).
 For parsing a PyTorch input file : 
 ```
 SOFIE::RModel model = SOFIE::PyTorch::Parse("PyTorchModel.pt",inputShapes);
 ```
 where `inputShapes` is a `std::vector<std::vector<size_t>>` defining the inputs shape tensors. This information is required by PyTorch since it is not stored in the model. 
-A full example for parsing a PyTorch file is in the [`TMVA_SOFIE_PyTorch.C`](https://root.cern.ch/doc/master/TMVA__SOFIE__PyTorch_8C.html) tutorial. 
+A full example for parsing a PyTorch file is in the [`TMVA_SOFIE_PyTorch.C`](https://root.cern/doc/master/TMVA__SOFIE__PyTorch_8C.html) tutorial. 
 
 For using the Keras and/or the PyTorch parser you need to have installed Keras and/or PyTorch in your Python system and in addition build root with the support for `pymva`, obtained when configuring with `-Dtmva-pymva=On`.