class RooMCStudy: public TNamed

 Construct Monte Carlo Study Manager. This class automates generating data from a given PDF,
 fitting the PDF to that data and accumulating the fit statistics.

 The constructor accepts the following arguments

 model       -- The PDF to be studied
 observables -- The variables of the PDF to be considered the observables

 Silence()                         -- Suppress all RooFit messages during running below PROGRESS level
 FitModel(const RooAbsPdf&)        -- The PDF for fitting, if it is different from the PDF for generating
 ConditionalObservables
           (const RooArgSet& set)  -- The set of observables that the PDF should _not_ be normalized over
 Binned(Bool_t flag)               -- Bin the dataset before fitting it. Speeds up fitting of large data samples
 FitOptions(const char*)           -- Classic fit options, provided for backward compatibility
 FitOptions(....)                  -- Options to be used for fitting. All named arguments inside FitOptions()
                                                   are passed to RooAbsPdf::fitTo();
 Verbose(Bool_t flag)              -- Activate informational messages in event generation phase
 Extended(Bool_t flag)             -- Determine number of events for each sample anew from a Poisson distribution
 Constrain(const RooArgSet& pars)  -- Apply internal constraints on given parameters in fit and sample constrained parameter
                                      values from constraint p.d.f for each toy.
 ExternalConstraints(const RooArgSet& ) -- Apply internal constraints on given parameters in fit and sample constrained parameter
                                      values from constraint p.d.f for each toy.
 ProtoData(const RooDataSet&,
                 Bool_t randOrder) -- Prototype data for the event generation. If the randOrder flag is
                                      set, the order of the dataset will be re-randomized for each generation
                                      cycle to protect against systematic biases if the number of generated
                                      events does not exactly match the number of events in the prototype dataset
                                      at the cost of reduced precision
                                      with mu equal to the specified number of events

 OBSOLETE, RETAINED FOR BACKWARD COMPATIBILY. PLEASE
 USE CONSTRUCTOR WITH NAMED ARGUMENTS

 Constructor with a generator and fit model. Both models may point
 to the same object. The 'dependents' set of variables is generated
 in the generator phase. The optional prototype dataset is passed to
 the generator

 Available generator options
  v  - Verbose
  e  - Extended: use Poisson distribution for Nevts generated

 Available fit options
  See RooAbsPdf::fitTo()

 Destructor

 Insert given RooMCStudy add-on module to the processing chain
 of this MCStudy object

 Run engine method. Generate and/or fit, according to flags, 'nSamples' samples of 'nEvtPerSample' events.
 If keepGenData is set, all generated data sets will be kept in memory and can be accessed
 later via genData().

 When generating, data sets will be written out in ascii form if the pattern string is supplied
 The pattern, which is a template for sprintf, should look something like "data/toymc_%04d.dat"
 and should contain one integer field that encodes the sample serial number.

 When fitting only, data sets may optionally be read from ascii files, using the same file
 pattern.

 Generate and fit 'nSamples' samples of 'nEvtPerSample' events.
 If keepGenData is set, all generated data sets will be kept in memory and can be accessed
 later via genData().

 Data sets will be written out is ascii form if the pattern string is supplied.
 The pattern, which is a template for sprintf, should look something like "data/toymc_%04d.dat"
 and should contain one integer field that encodes the sample serial number.

 Generate 'nSamples' samples of 'nEvtPerSample' events.
 If keepGenData is set, all generated data sets will be kept in memory
 and can be accessed later via genData().

 Data sets will be written out in ascii form if the pattern string is supplied.
 The pattern, which is a template for sprintf, should look something like "data/toymc_%04d.dat"
 and should contain one integer field that encodes the sample serial number.

 Fit 'nSamples' datasets, which are read from ASCII files.

 The ascii file pattern, which is a template for sprintf, should look something like "data/toymc_%04d.dat"
 and should contain one integer field that encodes the sample serial number.

 Fit 'nSamples' datasets, as supplied in 'dataSetList'

 Reset all fit parameters to the initial model
 parameters at the time of the RooMCStudy constructor

 Internal function. Performs actual fit according to specifications

 Redo fit on 'current' toy sample, or if genSample is not NULL
 do fit on given sample instead

 Internal method. Fit given dataset with fit model. If fit
 converges (TMinuit status code zero) The fit results are appended
 to the fit results dataset

 If the fit option "r" is supplied, the RooFitResult
 objects will always be saved, regardless of the
 fit status. RooFitResults objects can be retrieved
 later via fitResult().

 Utility function to add fit result from external fit to this RooMCStudy
 and process its results through the standard RooMCStudy statistics gathering tools.
 This function allows users to run the toy MC generation and/or fitting
 in a distributed way and to collect and analyze the results in a RooMCStudy
 as if they were run locally.

 This method is only functional if this RooMCStudy object is cleanm, i.e. it was not used
 to generate and/or fit any samples.

 Calculate the pulls for all fit parameters in
 the fit results data set, and add them to that dataset

 Return a RooDataSet the resulting fit parameters of each toy cycle.
 This dataset also contains any additional output that was generated
 by study modules that were added to this RooMCStudy

 Return an argset with the fit parameters for the given sample number

 NB: The fit parameters are only stored for successfull fits,
     thus the maximum sampleNum can be less that the number
     of generated samples and if so, the indeces will
     be out of synch with genData() and fitResult()

 Return the RooFitResult object of the fit to given sample

 Return the given generated dataset. This method will only return datasets
 if during the run cycle it was indicated that generator data should be saved.

 Plot the distribution of fitted values of a parameter. The parameter shown is the one from which the RooPlot
 was created, e.g.

 RooPlot* frame = param.frame(100,-10,10) ;
 mcstudy.paramOn(frame,LineStyle(kDashed)) ;

 Any named arguments passed to plotParamOn() are forwarded to the underlying plotOn() call

 Plot the distribution of the fitted value of the given parameter on a newly created frame.

 This function accepts the following optional arguments
 FrameRange(double lo, double hi) -- Set range of frame to given specification
 FrameBins(int bins)              -- Set default number of bins of frame to given number
 Frame(...)                       -- Pass supplied named arguments to RooAbsRealLValue::frame() function. See frame() function
                                     for list of allowed arguments

 If no frame specifications are given, the AutoRange() feature will be used to set the range
 Any other named argument is passed to the RooAbsData::plotOn() call. See that function for allowed options

 Plot the distribution of the fitted value of the given parameter on a newly created frame.

 This function accepts the following optional arguments
 FrameRange(double lo, double hi) -- Set range of frame to given specification
 FrameBins(int bins)              -- Set default number of bins of frame to given number
 Frame(...)                       -- Pass supplied named arguments to RooAbsRealLValue::frame() function. See frame() function
                                     for list of allowed arguments

 If no frame specifications are given, the AutoRange() feature will be used to set the range
 Any other named argument is passed to the RooAbsData::plotOn() call. See that function for allowed options

 Plot the distribution of the -log(L) values on a newly created frame.

 This function accepts the following optional arguments
 FrameRange(double lo, double hi) -- Set range of frame to given specification
 FrameBins(int bins)              -- Set default number of bins of frame to given number
 Frame(...)                       -- Pass supplied named arguments to RooAbsRealLValue::frame() function. See frame() function
                                     for list of allowed arguments

 If no frame specifications are given, the AutoRange() feature will be used to set the range
 Any other named argument is passed to the RooAbsData::plotOn() call. See that function for allowed options

 Plot the distribution of the fit errors for the specified parameter on a newly created frame.

 This function accepts the following optional arguments
 FrameRange(double lo, double hi) -- Set range of frame to given specification
 FrameBins(int bins)              -- Set default number of bins of frame to given number
 Frame(...)                       -- Pass supplied named arguments to RooAbsRealLValue::frame() function. See frame() function
                                     for list of allowed arguments

 If no frame specifications are given, the AutoRange() feature will be used to set the range
 Any other named argument is passed to the RooAbsData::plotOn() call. See that function for allowed options

 Plot the distribution of pull values for the specified parameter on a newly created frame. If asymmetric
 errors are calculated in the fit (by MINOS) those will be used in the pull calculation

 This function accepts the following optional arguments
 FrameRange(double lo, double hi) -- Set range of frame to given specification
 FrameBins(int bins)              -- Set default number of bins of frame to given number
 Frame(...)                       -- Pass supplied named arguments to RooAbsRealLValue::frame() function. See frame() function
                                     for list of allowed arguments
 FitGauss(Bool_t flag)            -- Add a gaussian fit to the frame

 If no frame specifications are given, the AutoSymRange() feature will be used to set the range
 Any other named argument is passed to the RooAbsData::plotOn() call. See that function for allowed options

 Internal function. Construct RooPlot from given parameter and modify the list of named
 arguments 'cmdList' to only contain the plot arguments that should be forwarded to
 RooAbsData::plotOn()

 Create a RooPlot of the -log(L) distribution in the range lo-hi
 with 'nBins' bins

 Create a RooPlot of the distribution of the fitted errors of the given parameter.
 The frame is created with a range [lo,hi] and plotted data will be binned in 'nbins' bins

 Create a RooPlot of the pull distribution for the given
 parameter.  The range lo-hi is plotted in nbins.  If fitGauss is
 set, an unbinned ML fit of the distribution to a Gaussian p.d.f
 is performed. The fit result is overlaid on the returned RooPlot
 and a box with the fitted mean and sigma is added.

 Return dataset with generator parameters for each toy. When constraints are used these
 may generally not be the same