class RooAbsPdf: public RooAbsReal

 Destructor

 Return current value, normalizated by integrating over
 the observables in 'nset'. If 'nset' is 0, the unnormalized value.
 is returned. All elements of 'nset' must be lvalues

 Unnormalized values are not cached
 Doing so would be complicated as _norm->getVal() could
 spoil the cache and interfere with returning the cached
 return value. Since unnormalized calls are typically
 done in integration calls, there is no performance hit.

 Analytical integral with normalization (see RooAbsReal::analyticalIntegralWN() for further information)

 This function applies the normalization specified by 'normSet' to the integral returned
 by RooAbsReal::analyticalIntegral(). The passthrough scenario (code=0) is also changed
 to return a normalized answer

 Check that passed value is positive and not 'not-a-number'.  If
 not, print an error, until the error counter reaches its set
 maximum.

 Return the integral of this PDF over all observables listed in 'nset'.

 Return pointer to RooAbsReal object that implements calculation of integral over observables iset in range
 rangeName, optionally taking the integrand normalized over observables nset

 Verify that the normalization integral cached with this PDF
 is valid for given set of normalization observables

 If not, the cached normalization integral (if any) is deleted
 and a new integral is constructed for use with 'nset'
 Elements in 'nset' can be discrete and real, but must be lvalues

 For functions that declare to be self-normalized by overloading the
 selfNormalized() function, a unit normalization is always constructed

 WVE 08/21/01 Probably obsolete now.

 Reset error counter to given value, limiting the number
 of future error messages for this pdf to 'resetValue'

 Reset trace counter to given value, limiting the
 number of future trace messages for this pdf to 'value'

 Return the log of the current value with given normalization
 An error message is printed if the argument of the log is negative.

 Returned the extended likelihood term (Nexpect - Nobserved*log(NExpected)
 of this PDF for the given number of observed events

 For successfull operation the PDF implementation must indicate
 it is extendable by overloading canBeExtended() and must
 implemented the expectedEvents() function.

 Construct representation of -log(L) of PDFwith given dataset. If dataset is unbinned, an unbinned likelihood is constructed. If the dataset
 is binned, a binned likelihood is constructed.

 The following named arguments are supported

 ConditionalObservables(const RooArgSet& set) -- Do not normalize PDF over listed observables
 Extended(Bool_t flag)           -- Add extended likelihood term, off by default
 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.
 SumCoefRange(const char* name)  -- Set the range in which to interpret the coefficients of RooAddPdf components
 NumCPU(int num)                 -- Parallelize NLL calculation on num CPUs
 Optimize(Bool_t flag)           -- Activate constant term optimization (on by default)
 SplitRange(Bool_t flag)         -- Use separate fit ranges in a simultaneous fit. Actual range name for each
                                    subsample is assumed to by rangeName_{indexState} where indexState
                                    is the state of the master index category of the simultaneous fit
 Contrain(const RooArgSet&pars)  -- Include constraints to listed parameters in likelihood using internal constrains in p.d.f
 ExternalConstraints(const RooArgSet& ) -- Include given external constraints to likelihood
 Verbose(Bool_t flag)           -- Constrols RooFit informational messages in likelihood construction

 Construct representation of -log(L) of PDFwith given dataset. If dataset is unbinned, an unbinned likelihood is constructed. If the dataset
 is binned, a binned likelihood is constructed.

 See RooAbsPdf::createNLL(RooAbsData& data, RooCmdArg arg1, RooCmdArg arg2, RooCmdArg arg3, RooCmdArg arg4,
                                    RooCmdArg arg5, RooCmdArg arg6, RooCmdArg arg7, RooCmdArg arg8)

 for documentation of options

 Fit PDF to given dataset. If dataset is unbinned, an unbinned maximum likelihood is performed. If the dataset
 is binned, a binned maximum likelihood is performed. By default the fit is executed through the MINUIT
 commands MIGRAD, HESSE and MINOS in succession.

 The following named arguments are supported

 Options to control construction of -log(L)

 ConditionalObservables(const RooArgSet& set) -- Do not normalize PDF over listed observables
 Extended(Bool_t flag)           -- Add extended likelihood term, off by default
 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.
 SumCoefRange(const char* name)  -- Set the range in which to interpret the coefficients of RooAddPdf components
 NumCPU(int num)                 -- Parallelize NLL calculation on num CPUs
 Optimize(Bool_t flag)           -- Activate constant term optimization (on by default)
 SplitRange(Bool_t flag)         -- Use separate fit ranges in a simultaneous fit. Actual range name for each
                                    subsample is assumed to by rangeName_{indexState} where indexState
                                    is the state of the master index category of the simultaneous fit
 Contrain(const RooArgSet&pars)  -- Include constraints to listed parameters in likelihood using internal constrains in p.d.f
 ExternalConstraints(const RooArgSet& ) -- Include given external constraints to likelihood

 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.

 Fit PDF to given dataset. If dataset is unbinned, an unbinned maximum likelihood is performed. If the dataset
 is binned, a binned maximum likelihood is performed. By default the fit is executed through the MINUIT
 commands MIGRAD, HESSE and MINOS in succession.

 See RooAbsPdf::fitTo(RooAbsData& data, RooCmdArg arg1, RooCmdArg arg2, RooCmdArg arg3, RooCmdArg arg4,
                                         RooCmdArg arg5, RooCmdArg arg6, RooCmdArg arg7, RooCmdArg arg8)

 for documentation of options

 OLD STYLE INTERFACE, PLEASE USE NEW INTERFACE fitTo(RooAbsData& data, RooCmdArg arg1,...,RooCmdArg arg8)

 Fit this PDF to given data set

 OLD STYLE INTERFACE, PLEASE USE NEW INTERFACE fitTo(RooAbsData& data, RooCmdArg arg1,...,RooCmdArg arg8)

 The dataset can be either binned, in which case a binned maximum likelihood fit
 is performed, or unbinned, in which case an unbinned maximum likelihood fit is performed

 Available fit options:

  "m" = MIGRAD only, i.e. no MINOS
  "s" = estimate step size with HESSE before starting MIGRAD
  "h" = run HESSE after MIGRAD
  "e" = Perform extended MLL fit
  "0" = Run MIGRAD with strategy MINUIT 0 (no correlation matrix calculation at end)
        Does not apply to HESSE or MINOS, if run afterwards.

  "q" = Switch off verbose mode
  "l" = Save log file with parameter values at each MINUIT step
  "v" = Show changed parameters at each MINUIT step
  "t" = Time fit
  "r" = Save fit output in RooFitResult object (return value is object RFR pointer)

 Available optimizer options

  "c" = Cache and precalculate components of PDF that exclusively depend on constant parameters
  "2" = Do NLL calculation in multi-processor mode on 2 processors
  "3" = Do NLL calculation in multi-processor mode on 3 processors
  "4" = Do NLL calculation in multi-processor mode on 4 processors

 The actual fit is performed to a temporary copy of both PDF and data set. Several optimization
 algorithm are run to increase the efficiency of the likelihood calculation and may increase
 the speed of complex fits up to an order of magnitude. All optimizations are exact, i.e the fit result
 of any fit should _exactly_ the same with and without optimization. We strongly encourage
 to stick to the default optimizer setting (all on). If for any reason you see a difference in the result
 with and without optimizer, please file a bug report.

 The function always return null unless the "r" fit option is specified. In that case a pointer to a RooFitResult
 is returned. The RooFitResult object contains the full fit output, including the correlation matrix.

 Print value of p.d.f, also print normalization integral that was last used, if any

 Print multi line detailed information of this RooAbsPdf

 Interface function to create a generator context from a p.d.f. This default
 implementation returns a 'standard' context that works for any p.d.f

 Generate a new dataset containing the specified variables with events sampled from our distribution.
 Generate the specified number of events or expectedEvents() if not specified.

 Any variables of this PDF that are not in whatVars will use their
 current values and be treated as fixed parameters. Returns zero
 in case of an error. The caller takes ownership of the returned
 dataset.

 The following named arguments are supported

 Verbose(Bool_t flag)               -- Print informational messages during event generation
 Extended()                         -- The actual number of events generated will be sampled from a Poisson distribution
                                       with mu=nevt. For use with extended maximum likelihood fits
 ProtoData(const RooDataSet& data,  -- Use specified dataset as prototype dataset. If randOrder is set to true
                 Bool_t randOrder)     the order of the events in the dataset will be read in a random order
                                       if the requested number of events to be generated does not match the
                                       number of events in the prototype dataset

 If ProtoData() is used, the specified existing dataset as a prototype: the new dataset will contain
 the same number of events as the prototype (unless otherwise specified), and any prototype variables not in
 whatVars will be copied into the new dataset for each generated event and also used to set our PDF parameters.
 The user can specify a  number of events to generate that will override the default. The result is a
 copy of the prototype dataset with only variables in whatVars randomized. Variables in whatVars that
 are not in the prototype will be added as new columns to the generated dataset.

 Generate a new dataset containing the specified variables with events sampled from our distribution.
 Generate the specified number of events or expectedEvents() if not specified.

 Any variables of this PDF that are not in whatVars will use their
 current values and be treated as fixed parameters. Returns zero
 in case of an error. The caller takes ownership of the returned
 dataset.

 The following named arguments are supported

 Name(const char* name)             -- Name of the output dataset
 Verbose(Bool_t flag)               -- Print informational messages during event generation
 NumEvent(int nevt)                 -- Generate specified number of events
 Extended()                         -- The actual number of events generated will be sampled from a Poisson distribution
                                       with mu=nevt. For use with extended maximum likelihood fits
 ProtoData(const RooDataSet& data,  -- Use specified dataset as prototype dataset. If randOrder is set to true
                 Bool_t randOrder,     the order of the events in the dataset will be read in a random order
                 Bool_t resample)      if the requested number of events to be generated does not match the
                                       number of events in the prototype dataset. If resample is also set to
                                       true, the prototype dataset will be resampled rather than be strictly
                                       reshuffled. In this mode events of the protodata may be used more than
                                       once.

 If ProtoData() is used, the specified existing dataset as a prototype: the new dataset will contain
 the same number of events as the prototype (unless otherwise specified), and any prototype variables not in
 whatVars will be copied into the new dataset for each generated event and also used to set our PDF parameters.
 The user can specify a  number of events to generate that will override the default. The result is a
 copy of the prototype dataset with only variables in whatVars randomized. Variables in whatVars that
 are not in the prototype will be added as new columns to the generated dataset.

 Generate a new dataset containing the specified variables with
 events sampled from our distribution. Generate the specified
 number of events or else try to use expectedEvents() if nEvents <= 0.
 Any variables of this PDF that are not in whatVars will use their
 current values and be treated as fixed parameters. Returns zero
 in case of an error. The caller takes ownership of the returned
 dataset.

 Generate a new dataset with values of the whatVars variables
 sampled from our distribution. Use the specified existing dataset
 as a prototype: the new dataset will contain the same number of
 events as the prototype (by default), and any prototype variables not in
 whatVars will be copied into the new dataset for each generated
 event and also used to set our PDF parameters. The user can specify a
 number of events to generate that will override the default. The result is a
 copy of the prototype dataset with only variables in whatVars
 randomized. Variables in whatVars that are not in the prototype
 will be added as new columns to the generated dataset.  Returns
 zero in case of an error. The caller takes ownership of the
 returned dataset.

 Return lookup table with randomized access order for prototype events,
 given nProto prototype data events and nGen events that will actually
 be accessed

 Load generatedVars with the subset of directVars that we can generate events for,
 and return a code that specifies the generator algorithm we will use. A code of
 zero indicates that we cannot generate any of the directVars (in this case, nothing
 should be added to generatedVars). Any non-zero codes will be passed to our generateEvent()
 implementation, but otherwise its value is arbitrary. The default implemetation of
 this method returns zero. Subclasses will usually implement this method using the
 matchArgs() methods to advertise the algorithms they provide.

 Interface for one-time initialization to setup the generator for the specified code.

 Interface for generation of anan event using the algorithm
 corresponding to the specified code. The meaning of each code is
 defined by the getGenerator() implementation. The default
 implementation does nothing.

 Check if given observable can be safely generated using the
 pdfs internal generator mechanism (if that existsP). Observables
 on which a PDF depends via more than route are not safe
 for use with internal generators because they introduce
 correlations not known to the internal generator

 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
 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
 NormRange(const char* name)     -- Calculate curve normalization w.r.t. only in specified ranges. NB: A Range() by default implies a NormRange()
                                    on the same range, but this option allows to override the default, or specify a normalization ranges
                                    when the full curve is to be drawn

 Misc content control

 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

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

 Helper function for plotting of composite p.d.fs. Given
 a set of selected components that should be plotted,
 find all nodes that (in)directly depend on these selected
 nodes. Mark all directly and indirecty selected nodes
 as 'selected' using the selectComp() method

 Plot oneself on 'frame'. In addition to features detailed in  RooAbsReal::plotOn(),
 the scale factor for a PDF can be interpreted in three different ways. The interpretation
 is controlled by ScaleType

  Relative  -  Scale factor is applied on top of PDF normalization scale factor
  NumEvent  -  Scale factor is interpreted as a number of events. The surface area
               under the PDF curve will match that of a histogram containing the specified
               number of event
  Raw       -  Scale factor is applied to the raw (projected) probability density.
               Not too useful, option provided for completeness.

 Add a box with parameter values (and errors) to the specified frame

 The following named arguments are supported

   Parameters(const RooArgSet& param) -- Only the specified subset of parameters will be shown.
                                         By default all non-contant parameters are shown
   ShowConstant(Bool_t flag)          -- Also display constant parameters
   Format(const char* optStr)         -- Classing [arameter formatting options, provided for backward compatibility
   Format(const char* what,...)       -- Parameter formatting options, details given below
   Label(const chat* label)           -- Add header label to parameter box
   Layout(Double_t xmin,              -- Specify relative position of left,right side of box and top of box. Position of
       Double_t xmax, Double_t ymax)     bottom of box is calculated automatically from number lines in box


 The Format(const char* what,...) has the following structure

   const char* what      -- Controls what is shown. "N" adds name, "E" adds error,
                            "A" shows asymmetric error, "U" shows unit, "H" hides the value
   FixedPrecision(int n) -- Controls precision, set fixed number of digits
   AutoPrecision(int n)  -- Controls precision. Number of shown digits is calculated from error
                            + n specified additional digits (1 is sensible default)

 Example use: pdf.paramOn(frame, Label("fit result"), Format("NEU",AutoPrecision(1)) ) ;

 OBSOLETE FUNCTION PROVIDED FOR BACKWARD COMPATIBILITY

 Add a text box with the current parameter values and their errors to the frame.
 Observables of this PDF appearing in the 'data' dataset will be omitted.

 Optional label will be inserted as first line of the text box. Use 'sigDigits'
 to modify the default number of significant digits printed. The 'xmin,xmax,ymax'
 values specify the inital relative position of the text box in the plot frame

 Return expected number of events from this p.d.f for use in extended
 likelihood calculations. This default implementation returns zero

 Change global level of verbosity for p.d.f. evaluations

 Return global level of verbosity for p.d.f. evaluations

 Return a p.d.f that represent a projection of this p.d.f integrated over given observables

 Create a cumulative distribution function of this p.d.f in terms
 of the observables listed in iset. If no nset argument is given
 the c.d.f normalization is constructed over the integrated
 observables, so that its maximum value is precisely 1. It is also
 possible to choose a different normalization for
 multi-dimensional p.d.f.s: eg. for a pdf f(x,y,z) one can
 construct a partial cdf c(x,y) that only when integrated itself
 over z results in a maximum value of 1. To construct such a cdf pass
 z as argument to the optional nset argument

 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

 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

 This helper function finds and collects all constraints terms of all coponent p.d.f.s
 and returns a RooArgSet with all those terms

 Clear the evaluation error flag

 Return the evaluation error flag

 Raise the evaluation error flag

 Constraint management

 Get p.d.f normalization term needed for observables 'nset'

 If true, p.d.f is taken as self-normalized and no attempt is made to add a normalization term
 This default implementation return false

 Returns ability of p.d.f to provided extended likelihood terms. Possible
 answers are CanNotBeExtended, CanBeExtended or MustBeExtended. This
 default implementation always return CanNotBeExtended

 If true p.d.f can provide extended likelihood term

 If true p.d.f must extended likelihood term

 Hook function intercepting redirectServer calls. Discard current normalization
 object if any server is redirected