class RooAbsReal: public RooAbsArg

 Destructor

 Equality operator comparing to a Double_t

 Equality operator when comparing to another RooAbsArg.
 Only functional when the other arg is a RooAbsReal

 Return this variable's title string. If appendUnit is true and
 this variable has units, also append a string " (<unit>)".

 Return value of object. If the cache is clean, return the
 cached value, otherwise recalculate on the fly and refill
 the cache

 Calculate current value of object, with error tracing wrapper

 Variant of getAnalyticalIntegral that is also passed the normalization set
 that should be applied to the integrand of which the integral is request.
 For certain operator p.d.f it is useful to overload this function rather
 than analyticalIntegralWN() as the additional normalization information
 may be useful in determining a more efficient decomposition of the
 requested integral

 Interface function getAnalyticalIntergral advertises the
 analytical integrals that are supported. 'integSet'
 is the set of dependents for which integration is requested. The
 function should copy the subset of dependents it can analytically
 integrate to anaIntSet and return a unique identification code for
 this integration configuration.  If no integration can be
 performed, zero should be returned.

 Implements the actual analytical integral(s) advertised by
 getAnalyticalIntegral.  This functions will only be called with
 codes returned by getAnalyticalIntegral, except code zero.

 Implements the actual analytical integral(s) advertised by
 getAnalyticalIntegral.  This functions will only be called with
 codes returned by getAnalyticalIntegral, except code zero.

 Get the label associated with the variable

 Set the label associated with this variable

Read object contents from stream (dummy for now)

Write object contents to stream (dummy for now)

 Print object value

 Structure printing

 Check if current value is valid

 Interface function to check if given value is a valid value for this object.
 This default implementation considers all values valid

 Create a RooProfileLL object that eliminates all nuisance parameters in the
 present function. The nuisance parameters are defined as all parameters
 of the function except the stated paramsOfInterest

 Create an object that represents the integral of the function over one or more observables listed in iset
 The actual integration calculation is only performed when the return object is evaluated. The name
 of the integral object is automatically constructed from the name of the input function, the variables
 it integrates and the range integrates over

 The following named arguments are accepted

 NormSet(const RooArgSet&)            -- Specify normalization set, mostly useful when working with PDFS
 NumIntConfig(const RooNumIntConfig&) -- Use given configuration for any numeric integration, if necessary
 Range(const char* name)              -- Integrate only over given range. Multiple ranges may be specified
                                         by passing multiple Range() arguments

 Create an object that represents the integral of the function over one or more observables listed in iset
 The actual integration calculation is only performed when the return object is evaluated. The name
 of the integral object is automatically constructed from the name of the input function, the variables
 it integrates and the range integrates over. If nset is specified the integrand is request
 to be normalized over nset (only meaningful when the integrand is a pdf). If rangename is specified
 the integral is performed over the named range, otherwise it is performed over the domain of each
 integrated observable. If cfg is specified it will be used to configure any numeric integration
 aspect of the integral. It will not force the integral to be performed numerically, which is
 decided automatically by RooRealIntegral

 Utility function for createIntegral that creates the actual integreal object

 Utility function for createIntObj() that aids in the construct of recursive integrals
 over functions with multiple observables with parameterized ranges. This function
 finds in a given set allObs over which integration is requested the largeset subset
 of observables that can be integrated simultaneously. This subset consists of
 observables with fixed ranges and observables with parameterized ranges whose
 parameterization does not depend on any observable that is also integrated.

 Construct string with unique suffix name to give to integral object that encodes
 integrated observables, normalization observables and the integration range name

 Utility function for plotOn() that creates a projection of a function or p.d.f
 to be plotted on a RooPlot.

 Utility function for plotOn() that creates a projection of a function or p.d.f
 to be plotted on a RooPlot.

 Utility function for plotOn() that creates a projection of a function or p.d.f
 to be plotted on a RooPlot.

 Create a new object G that represents the normalized projection:

             Integral [ F[x,y,p] , { y } ]
  G[x,p] = ---------------------------------
            Integral [ F[x,y,p] , { x,y } ]

 where F[x,y,p] is the function we represent, "x" are the
 specified dependentVars, "y" are the specified projectedVars, and
 "p" are our remaining variables ("parameters"). Return a
 pointer to the newly created object, or else zero in case of an
 error.  The caller is responsible for deleting the contents of
 cloneSet (which includes the returned projection object)

 Fill the ROOT histogram 'hist' with values sampled from this
 function at the bin centers.  Our value is calculated by first
 integrating out any variables in projectedVars and then scaling
 the result by scaleFactor. Returns a pointer to the input
 histogram, or zero in case of an error. The input histogram can
 be any TH1 subclass, and therefore of arbitrary
 dimension. Variables are matched with the (x,y,...) dimensions of
 the input histogram according to the order in which they appear
 in the input plotVars list. If scaleForDensity is true the
 histogram is filled with a the functions density rather than
 the functions value (i.e. the value at the bin center is multiplied
 with bin volume)

 Fill a RooDataHist with values sampled from this function at the
 bin centers.  If extendedMode is true, the p.d.f. values is multiplied
 by the number of expected events in each bin

 An optional scaling by a given scaleFactor can be performed.
 Returns a pointer to the input RooDataHist, or zero
 in case of an error.

 If correctForBinSize is true the RooDataHist
 is filled with the functions density (function value times the
 bin volume) rather than function value.

 If showProgress is true
 a process indicator is printed on stdout in steps of one percent,
 which is mostly useful for the sampling of expensive functions
 such as likelihoods

 Create and fill a ROOT histogram TH1,TH2 or TH3 with the values of this function for the variables with given names
 The number of bins can be controlled using the [xyz]bins parameters. For a greater degree of control
 use the createHistogram() method below with named arguments

 The caller takes ownership of the returned histogram

 Create and fill a ROOT histogram TH1,TH2 or TH3 with the values of this function.

 This function accepts the following arguments

 name -- Name of the ROOT histogram
 xvar -- Observable to be mapped on x axis of ROOT histogram

 Binning(const char* name)                    -- Apply binning with given name to x axis of histogram
 Binning(RooAbsBinning& binning)              -- Apply specified binning to x axis of histogram
 Binning(int nbins, [double lo, double hi])   -- Apply specified binning to x axis of histogram
 ConditionalObservables(const RooArgSet& set) -- Do not normalized PDF over following observables when projecting PDF into histogram
 Scaling(Bool_t)                              -- Apply density-correction scaling (multiply by bin volume), default is kTRUE

 YVar(const RooAbsRealLValue& var,...)    -- Observable to be mapped on y axis of ROOT histogram
 ZVar(const RooAbsRealLValue& var,...)    -- Observable to be mapped on z axis of ROOT histogram

 The YVar() and ZVar() arguments can be supplied with optional Binning() arguments to control the binning of the Y and Z axes, e.g.
 createHistogram("histo",x,Binning(-1,1,20), YVar(y,Binning(-1,1,30)), ZVar(z,Binning("zbinning")))

 The caller takes ownership of the returned histogram

 Internal method implementing createHistogram

 Plot (project) PDF on specified frame. If a PDF is plotted in an empty frame, it
 will show a unit normalized curve in the frame variable, taken at the present value
 of other observables defined for this PDF

 If a PDF is plotted in a frame in which a dataset has already been plotted, it will
 show a projected curve integrated over all variables that were present in the shown
 dataset except for the one on the x-axis. The normalization of the curve will also
 be adjusted to the event count of the plotted dataset. An informational message
 will be printed for each projection step that is performed

 This function takes the following named arguments

 Projection control

 Slice(const RooArgSet& set)     -- Override default projection behaviour by omittting observables listed
                                    in set from the projection, resulting a 'slice' plot. Slicing is usually
                                    only sensible in discrete observables. The slice is position at the 'current'
                                    value of the observable objects

 Slice(RooCategory& cat,         -- Override default projection behaviour by omittting specified category
       const char* label)           observable from the projection, resulting in a 'slice' plot. The slice is positioned
                                    at the given label value. Multiple Slice() commands can be given to specify slices
                                    in multiple observables

 Project(const RooArgSet& set)   -- Override default projection behaviour by projecting over observables
                                    given in set and complete ignoring the default projection behavior. Advanced use only.

 ProjWData(const RooAbsData& d)  -- Override default projection _technique_ (integration). For observables present in given dataset
                                    projection of PDF is achieved by constructing an average over all observable values in given set.
                                    Consult RooFit plotting tutorial for further explanation of meaning & use of this technique

 ProjWData(const RooArgSet& s,   -- As above but only consider subset 's' of observables in dataset 'd' for projection through data averaging
           const RooAbsData& d)

 ProjectionRange(const char* rn) -- Override default range of projection integrals to a different range speficied by given range name.
                                    This technique allows you to project a finite width slice in a real-valued observable

 NumCPU(Int_t ncpu)              -- Number of CPUs to use simultaneously to calculate data-weighted projections (only in combination with ProjWData)


 Misc content control

 PrintEvalErrors(Int_t numErr)   -- Control number of p.d.f evaluation errors printed per curve. A negative
                                    value suppress output completely, a zero value will only print the error count per p.d.f component,
                                    a positive value is will print details of each error up to numErr messages per p.d.f component.

 EvalErrorValue(Double_t value)  -- Set curve points at which (pdf) evaluation error occur to specified value. By default the
                                    function value is plotted.

 Normalization(Double_t scale,   -- Adjust normalization by given scale factor. Interpretation of number depends on code: Relative:
                ScaleType code)     relative adjustment factor, NumEvent: scale to match given number of events.

 Name(const chat* name)          -- Give curve specified name in frame. Useful if curve is to be referenced later

 Asymmetry(const RooCategory& c) -- Show the asymmetry of the PDF in given two-state category [F(+)-F(-)] / [F(+)+F(-)] rather than
                                    the PDF projection. Category must have two states with indices -1 and +1 or three states with
                                    indeces -1,0 and +1.

 ShiftToZero(Bool_t flag)        -- Shift entire curve such that lowest visible point is at exactly zero. Mostly useful when
                                    plotting -log(L) or chi^2 distributions

 AddTo(const char* name,         -- Add constructed projection to already existing curve with given name and relative weight factors
                                    double_t wgtSelf, double_t wgtOther)

 Plotting control

 DrawOption(const char* opt)     -- Select ROOT draw option for resulting TGraph object

 LineStyle(Int_t style)          -- Select line style by ROOT line style code, default is solid

 LineColor(Int_t color)          -- Select line color by ROOT color code, default is blue

 LineWidth(Int_t width)          -- Select line with in pixels, default is 3

 FillStyle(Int_t style)          -- Select fill style, default is not filled. If a filled style is selected, also use VLines()
                                    to add vertical downward lines at end of curve to ensure proper closure
 FillColor(Int_t color)          -- Select fill color by ROOT color code

 Range(const char* name)         -- Only draw curve in range defined by given name

 Range(double lo, double hi)     -- Only draw curve in specified range

 VLines()                        -- Add vertical lines to y=0 at end points of curve

 Precision(Double_t eps)         -- Control precision of drawn curve w.r.t to scale of plot, default is 1e-3. Higher precision
                                    will result in more and more densely spaced curve points

 Invisible(Bool_t flag)           -- Add curve to frame, but do not display. Useful in combination AddTo()

 VisualizeError(const RooFitResult& fitres, Double_t Z=1, Bool_t linearMethod=kTRUE)
                                  -- Visualize the uncertainty on the parameters, as given in fitres, at 'Z' sigma'

 VisualizeError(const RooFitResult& fitres, const RooArgSet& param, Double_t Z=1, Bool_t linearMethod=kTRUE) ;
                                  -- Visualize the uncertainty on the subset of parameters 'param', as given in fitres, at 'Z' sigma'


 Details on error band visualization


 By default (linMethod=kTRUE) a linearized error is shown which is calculated as follows
                                    T
 error(x) = Z* F_a(x) * Corr(a,a') F_a'(x)

 where     F_a(x) = [ f(x,a+da) - f(x,a-da) ] / 2, with f(x) the plotted curve and 'da' taken from the fit result
       Corr(a,a') = the correlation matrix from the fit result
                Z = requested significance 'Z sigma band'

 The linear method is fast (required 2*N evaluations of the curve, where N is the number of parameters), but may
 not be accurate in the presence of strong correlations (~>0.9) and at Z>2 due to linear and Gaussian approximations made

 Alternatively (linMethod=kFALSE), a more robust error is calculated using a sampling method. In this method a number of curves
 is calculated with variations of the parameter values, as drawn from a multi-variate Gaussian p.d.f. that is constructed
 from the fit results covariance matrix. The error(x) is determined by calculating a central interval that capture N% of the variations
 for each valye of x, where N% is controlled by Z (i.e. Z=1 gives N=68%). The number of sampling curves is chosen to be such
 that at least 30 curves are expected to be outside the N% interval, and is minimally 100 (e.g. Z=1->Ncurve=100, Z=2->Ncurve=659, Z=3->Ncurve=11111)
 Intervals from the sampling method can be asymmetric, and may perform better in the presence of strong correlations, but may take (much)
 longer to calculate

 Internal back-end function of plotOn() with named arguments

 Plotting engine function for internal use

 Plot ourselves on given frame. If frame contains a histogram, all dimensions of the plotted
 function that occur in the previously plotted dataset are projected via partial integration,
 otherwise no projections are performed. Optionally, certain projections can be performed
 by summing over the values present in a provided dataset ('projData'), to correctly
 project out data dependents that are not properly described by the PDF (e.g. per-event errors).

 The functions value can be multiplied with an optional scale factor. The interpretation
 of the scale factor is unique for generic real functions, for PDFs there are various interpretations
 possible, which can be selection with 'stype' (see RooAbsPdf::plotOn() for details).

 The default projection behaviour can be overriden by supplying an optional set of dependents
 to project. For most cases, plotSliceOn() and plotProjOn() provide a more intuitive interface
 to modify the default projection behavour.

 OBSOLETE -- RETAINED FOR BACKWARD COMPATIBILITY. Use the plotOn(frame,Slice(...)) instead

 Plotting engine for asymmetries. Implements the functionality if plotOn(frame,Asymmetry(...)))

 Plot asymmetry of ourselves, defined as

   asym = f(asymCat=-1) - f(asymCat=+1) / ( f(asymCat=-1) + f(asymCat=+1) )

 on frame. If frame contains a histogram, all dimensions of the plotted
 asymmetry function that occur in the previously plotted dataset are projected via partial integration.
 Otherwise no projections are performed,

 The asymmetry function can be multiplied with an optional scale factor. The default projection
 behaviour can be overriden by supplying an optional set of dependents to project.

 Plot function or p.d.f. on frame with support for visualization of the uncertainty encoded in the given fit result fr.
 If params is non-zero, only the subset of the parameters in fr that occur in params is considered for the error evaluation
 Argument argList can contain any RooCmdArg named argument that can be applied to a regular plotOn() operation

 By default (linMethod=kTRUE) a linearized error is shown which is calculated as follows
                                    T
 error(x) = Z* F_a(x) * Corr(a,a') F_a'(x)

 where     F_a(x) = [ f(x,a+da) - f(x,a-da) ] / 2, with f(x) the plotted curve and 'da' taken from the fit result
       Corr(a,a') = the correlation matrix from the fit result
                Z = requested signifance 'Z sigma band'

 The linear method is fast (required 2*N evaluations of the curve, where N is the number of parameters), but may
 not be accurate in the presence of strong correlations (~>0.9) and at Z>2 due to linear and Gaussian approximations made

 Alternatively, a more robust error is calculated using a sampling method. In this method a number of curves
 is calculated with variations of the parameter values, as drawn from a multi-variate Gaussian p.d.f. that is constructed
 from the fit results covariance matrix. The error(x) is determined by calculating a central interval that capture N% of the variations
 for each valye of x, where N% is controlled by Z (i.e. Z=1 gives N=68%). The number of sampling curves is chosen to be such
 that at least 30 curves are expected to be outside the N% interval, and is minimally 100 (e.g. Z=1->Ncurve=100, Z=2->Ncurve=659, Z=3->Ncurve=11111)
 Intervals from the sampling method can be asymmetric, and may perform better in the presence of strong correlations

 Utility function for plotOn(), perform general sanity check on frame to ensure safe plotting operations

 Utility function for plotOn() that constructs the set of
 observables to project when plotting ourselves as function of
 'plotVar'. 'allVars' is the list of variables that must be
 projected, but may contain variables that we do not depend on. If
 'silent' is cleared, warnings about inconsistent input parameters
 will be printed.

 If true, the current pdf is a selected component (for use in plotting)

 Global switch controlling the activation of the selectComp() functionality

 Create an interface adaptor f(vars) that binds us to the specified variables
 (in arbitrary order). For example, calling bindVars({x1,x3}) on an object
 F(x1,x2,x3,x4) returns an object f(x1,x3) that is evaluated using the
 current values of x2 and x4. The caller takes ownership of the returned adaptor.

 Copy the cached value of another RooAbsArg to our cache.
 Warning: This function copies the cached values of source,
 it is the callers responsibility to make sure the cache is clean

 Attach object to a branch of given TTree. By default it will
 register the internal value cache RooAbsReal::_value as branch
 buffer for a Double_t tree branch with the same name as this
 object. If no Double_t branch is found with the name of this
 object, this method looks for a Float_t Int_t, UChar_t and UInt_t
 branch in that order. If any of these are found the buffer for
 that branch is set to a correctly typed conversion buffer in this
 RooRealVar.  A flag is set that will cause copyCache to copy the
 object value from the appropriate conversion buffer instead of
 the _value buffer.

 Fill the tree branch that associated with this object with its current value

 (De)Activate associated tree branch

 Create a RooRealVar fundamental object with our properties. The new
 object will be created without any fit limits.

 Utility function for use in getAnalyticalIntegral(). If the
 content of proxy 'a' occurs in set 'allDeps' then the argument
 held in 'a' is copied from allDeps to analDeps

 Utility function for use in getAnalyticalIntegral(). If the
 contents of proxies a,b occur in set 'allDeps' then the arguments
 held in a,b are copied from allDeps to analDeps

 Utility function for use in getAnalyticalIntegral(). If the
 contents of proxies a,b,c occur in set 'allDeps' then the arguments
 held in a,b,c are copied from allDeps to analDeps

 Utility function for use in getAnalyticalIntegral(). If the
 contents of proxies a,b,c,d occur in set 'allDeps' then the arguments
 held in a,b,c,d are copied from allDeps to analDeps

 Utility function for use in getAnalyticalIntegral(). If the
 contents of 'refset' occur in set 'allDeps' then the arguments
 held in 'refset' are copied from allDeps to analDeps.

 Check if allArgs contains matching elements for each name in nameList. If it does,
 add the corresponding args from allArgs to matchedArgs and return kTRUE. Otherwise
 return kFALSE and do not change matchedArgs.

 Returns the default numeric integration configuration for all RooAbsReals

 Returns the specialized integrator configuration for _this_ RooAbsReal.
 If this object has no specialized configuration, a null pointer is returned.

 Returns the specialized integrator configuration for _this_ RooAbsReal.
 If this object has no specialized configuration, a null pointer is returned,
 unless createOnTheFly is kTRUE in which case a clone of the default integrator
 configuration is created, installed as specialized configuration, and returned

 Return the numeric integration configuration used for this object. If
 a specialized configuration was associated with this object, that configuration
 is returned, otherwise the default configuration for all RooAbsReals is returned

 Return the numeric integration configuration used for this object. If
 a specialized configuration was associated with this object, that configuration
 is returned, otherwise the default configuration for all RooAbsReals is returned

 Set the given integrator configuration as default numeric integration
 configuration for this object

 Remove the specialized numeric integration configuration associated
 with this object

 Interface function to force use of a given set of observables
 to interpret function value. Needed for functions or p.d.f.s
 whose shape depends on the choice of normalization such as
 RooAddPdf

 Interface function to force use of a given normalization range
 to interpret function value. Needed for functions or p.d.f.s
 whose shape depends on the choice of normalization such as
 RooAddPdf

 Activate cache validation mode

 Advertise capability to determine maximum value of function for given set of
 observables. If no direct generator method is provided, this information
 will assist the accept/reject generator to operate more efficiently as
 it can skip the initial trial sampling phase to empirically find the function
 maximum

 Return maximum value for set of observables identified by code assigned
 in getMaxVal

 Interface to insert remote error logging messages received by RooRealMPFE into current error loggin stream

 Log evaluation error message. Evaluation errors may be routed through a different
 protocol than generic RooFit warning message (which go straight through RooMsgService)
 because evaluation errors can occur in very large numbers in the use of likelihood
 evaluations. In logEvalError mode, controlled by global method enableEvalErrorLogging()
 messages reported through this function are not printed but all stored in a list,
 along with server values at the time of reporting. Error messages logged in this
 way can be printed in a structured way, eliminating duplicates and with the ability
 to truncate the list by printEvalErrors. This is the standard mode of error logging
 during MINUIT operations. If enableEvalErrorLogging() is false, all errors
 reported through this method are passed for immediate printing through RooMsgService.
 A string with server names and values is constructed automatically for error logging
 purposes, unless a custom string with similar information is passed as argument.

 Clear the stack of evaluation error messages

 Print all outstanding logged evaluation error on the given ostream. If maxPerNode
 is zero, only the number of errors for each source (object with unique name) is listed.
 If maxPerNode is greater than zero, up to maxPerNode detailed error messages are shown
 per source of errors. A truncation message is shown if there were more errors logged
 than shown.

 Return the number of logged evaluation errors since the last clearing.

 Fix the interpretation of the coefficient of any RooAddPdf component in
 the expression tree headed by this object to the given set of observables.

 If the force flag is false, the normalization choice is only fixed for those
 RooAddPdf components that have the default 'automatic' interpretation of
 coefficients (i.e. the interpretation is defined by the observables passed
 to getVal()). If force is true, also RooAddPdf that already have a fixed
 interpretation are changed to a new fixed interpretation.

 Fix the interpretation of the coefficient of any RooAddPdf component in
 the expression tree headed by this object to the given set of observables.

 If the force flag is false, the normalization range choice is only fixed for those
 RooAddPdf components that currently use the default full domain to interpret their
 coefficients. If force is true, also RooAddPdf that already have a fixed
 interpretation range are changed to a new fixed interpretation range.

 Interface method for function objects to indicate their prefferred order of observables
 for scanning their values into a (multi-dimensional) histogram or RooDataSet. The observables
 to be ordered are offered in argument 'obs' and should be copied in their preferred
 order into argument 'orderdObs', This default implementation indicates no preference
 and copies the original order of 'obs' into 'orderedObs'

 Create a running integral over this function, i.e. given a f(x), create an object
 representing 'int[x_lo,x] f(x_prime) dx_prime'

 Create an object that represents the running integral of the function over one or more observables listed in iset, i.e.

   int[x_lo,x] f(x_prime) dx_prime

 The actual integration calculation is only performed when the return object is evaluated. The name
 of the integral object is automatically constructed from the name of the input function, the variables
 it integrates and the range integrates over. The default strategy to calculate the running integrals is

   - If the integrand (this object) supports analytical integration, construct an integral object
     that calculate the running integrals value by calculating the analytical integral each
     time the running integral object is evaluated

   - If the integrand (this object) requires numeric integration to construct the running integral
     create an object of class RooNumRunningInt which first samples the entire function and integrates
     the sampled function numerically. This method has superior performance as there is no need to
     perform a full (numeric) integration for each evaluation of the running integral object, but
     only when one of its parameters has changed.

 The choice of strategy can be changed with the ScanAll() argument, which forces the use of the
 scanning technique implemented in RooNumRunningInt for all use cases, and with the ScanNone()
 argument which forces the 'integrate each evaluation' technique for all use cases. The sampling
 granularity for the scanning technique can be controlled with the ScanParameters technique
 which allows to specify the number of samples to be taken, and to which order the resulting
 running integral should be interpolated. The default values are 1000 samples and 2nd order
 interpolation.

 The following named arguments are accepted

 SupNormSet(const RooArgSet&)         -- Observables over which should be normalized _in_addition_ to the
                                         integration observables
 ScanParameters(Int_t nbins,          -- Parameters for scanning technique of making CDF: number
                Int_t intOrder)          of sampled bins and order of interpolation applied on numeric cdf
 ScanNum()                            -- Apply scanning technique if cdf integral involves numeric integration
 ScanAll()                            -- Always apply scanning technique
 ScanNone()                           -- Never apply scanning technique

 Utility function for createRunningIntegral that construct an object
 implementing the numeric scanning technique for calculating the running integral

 Utility function for createRunningIntegral that construct an
 object implementing the standard (analytical) integration
 technique for calculating the running integral

 Return a RooFunctor object bound to this RooAbsReal with given definition of observables
 and parameters

 Return a ROOT TF1,2,3 object bound to this RooAbsReal with given definition of observables
 and parameters

 Return function representing first, second or third order derivative of this function

 Perform a chi^2 fit to given histogram By default the fit is executed through the MINUIT
 commands MIGRAD, HESSE in succession

 The following named arguments are supported

 Options to control construction of -log(L)

 Range(const char* name)         -- Fit only data inside range with given name
 Range(Double_t lo, Double_t hi) -- Fit only data inside given range. A range named "fit" is created on the fly on all observables.
                                    Multiple comma separated range names can be specified.
 NumCPU(int num)                 -- Parallelize NLL calculation on num CPUs
 Optimize(Bool_t flag)           -- Activate constant term optimization (on by default)

 Options to control flow of fit procedure

 InitialHesse(Bool_t flag)      -- Flag controls if HESSE before MIGRAD as well, off by default
 Hesse(Bool_t flag)             -- Flag controls if HESSE is run after MIGRAD, on by default
 Minos(Bool_t flag)             -- Flag controls if MINOS is run after HESSE, on by default
 Minos(const RooArgSet& set)    -- Only run MINOS on given subset of arguments
 Save(Bool_t flag)              -- Flac controls if RooFitResult object is produced and returned, off by default
 Strategy(Int_t flag)           -- Set Minuit strategy (0 through 2, default is 1)
 FitOptions(const char* optStr) -- Steer fit with classic options string (for backward compatibility). Use of this option
                                   excludes use of any of the new style steering options.

 Options to control informational output

 Verbose(Bool_t flag)           -- Flag controls if verbose output is printed (NLL, parameter changes during fit
 Timer(Bool_t flag)             -- Time CPU and wall clock consumption of fit steps, off by default
 PrintLevel(Int_t level)        -- Set Minuit print level (-1 through 3, default is 1). At -1 all RooFit informational
                                   messages are suppressed as well
 Warnings(Bool_t flag)          -- Enable or disable MINUIT warnings (enabled by default)
 PrintEvalErrors(Int_t numErr)  -- Control number of p.d.f evaluation errors printed per likelihood evaluation. A negative
                                   value suppress output completely, a zero value will only print the error count per p.d.f component,
                                   a positive value is will print details of each error up to numErr messages per p.d.f component.

 Internal back-end function to steer chi2 fits

 Create a chi-2 from a histogram and this function.

 The following named arguments are supported

  Options to control construction of the chi^2

  DataError(RooAbsData::ErrorType)  -- Choose between Poisson errors and Sum-of-weights errors
  NumCPU(Int_t)                     -- Activate parallel processing feature on N processes
  Range()                           -- Calculate Chi2 only in selected region

 Internal back-end function to create a chi2

 Create a chi-2 from a series of x and y value stored in a dataset.
 The y values can either be the event weights, or can be another column designated
 by the YVar() argument. The y value must have errors defined for the chi-2 to
 be well defined.

 The following named arguments are supported

 Options to control construction of the chi^2

 YVar(RooRealVar& yvar)          -- Designate given column in dataset as Y value
 Integrate(Bool_t flag)          -- Integrate function over range specified by X errors
                                    rather than take value at bin center.

 Options to control flow of fit procedure

 InitialHesse(Bool_t flag)      -- Flag controls if HESSE before MIGRAD as well, off by default
 Hesse(Bool_t flag)             -- Flag controls if HESSE is run after MIGRAD, on by default
 Minos(Bool_t flag)             -- Flag controls if MINOS is run after HESSE, on by default
 Minos(const RooArgSet& set)    -- Only run MINOS on given subset of arguments
 Save(Bool_t flag)              -- Flac controls if RooFitResult object is produced and returned, off by default
 Strategy(Int_t flag)           -- Set Minuit strategy (0 through 2, default is 1)
 FitOptions(const char* optStr) -- Steer fit with classic options string (for backward compatibility). Use of this option
                                   excludes use of any of the new style steering options.

 Options to control informational output

 Verbose(Bool_t flag)           -- Flag controls if verbose output is printed (NLL, parameter changes during fit
 Timer(Bool_t flag)             -- Time CPU and wall clock consumption of fit steps, off by default
 PrintLevel(Int_t level)        -- Set Minuit print level (-1 through 3, default is 1). At -1 all RooFit informational
                                   messages are suppressed as well
 Warnings(Bool_t flag)          -- Enable or disable MINUIT warnings (enabled by default)
 PrintEvalErrors(Int_t numErr)  -- Control number of p.d.f evaluation errors printed per likelihood evaluation. A negative
                                   value suppress output completely, a zero value will only print the error count per p.d.f component,
                                   a positive value is will print details of each error up to numErr messages per p.d.f component.

 Internal back-end function to steer chi2 fits

 Create a chi-2 from a series of x and y value stored in a dataset.
 The y values can either be the event weights (default), or can be another column designated
 by the YVar() argument. The y value must have errors defined for the chi-2 to
 be well defined.

 The following named arguments are supported

 Options to control construction of the chi^2

 YVar(RooRealVar& yvar)          -- Designate given column in dataset as Y value
 Integrate(Bool_t flag)          -- Integrate function over range specified by X errors
                                    rather than take value at bin center.

 Internal back-end function to create a chi^2 from a function and a dataset

 Internal driver function for chi2 fits

 Return value and unit accessors

 Return string with unit description

 Set unit description to given string

 Interface to force RooRealIntegral to offer given observable for internal integration
 even if this is deemed unsafe. This default implementation returns always flase

 If flag is true, all advertised analytical integrals will be ignored
 and all integrals are calculated numerically

 Create integral over observables in iset in range named rangeName

 Create integral over observables in iset in range named rangeName with integrand normalized over observables in nset

 Return default level for MINUIT error analysis

 Interface for returning an optional hint for initial sampling points when constructing a curve
 projected on observable.

 Hook function to add functionality to evaluation tracing in derived classes

 If flag is true, only selected component will be included in evaluates of RooAddPdf components