class TRolke: public TObject

constructor with optional Confidence Level argument.
'option' is not used.

 Model 1: Background - Poisson, Efficiency - Binomial
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    z   : number of MC events observed
    tau : ratio parameter (read TRolke.cxx for details)
    m   : number of MC events generated

 Model 2: Background - Poisson, Efficiency - Gaussian
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    em  : estimate of the efficiency
    tau : ratio parameter (read TRolke.cxx for details)
    sde : efficiency estimate's standard deviation

 Model 3: Background - Gaussian, Efficiency - Gaussian (x,bm,em,sde,sdb)
    x   : number of observed events in the experiment
    bm  : estimate of the background
    em  : estimate of the efficiency
    sde : efficiency estimate's standard deviation
    sdb : background estimate's standard deviation

 Model 4: Background - Poisson, Efficiency - known     (x,y,tau,e)
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    tau : ratio parameter (read TRolke.cxx for details)
    e   : true efficiency (considered known)

 Model 5: Background - Gaussian, Efficiency - known    (x,bm,sdb,e
    x   : number of observed events in the experiment
    bm  : estimate of the background
    sdb : background estimate's standard deviation
    e   : true efficiency (considered known)

 Model 6: Background - known, Efficiency - Binomial    (x,z,m,b)
    x   : number of observed events in the experiment
    z   : number of MC events observed
    m   : number of MC events generated
    b   : background expectation value (considered known)

 Model 7: Background - known, Efficiency - Gaussian    (x,em,sde,b)
    x   : number of observed events in the experiment
    em  : estimate of the efficiency
    sde : efficiency estimate's standard deviation
    b   : background expectation value (considered known)

Calculate and get the upper and lower limits for the pre-specified model

Calculate and get upper limit for the pre-specified model

Calculate and get lower limit for the pre-specified model

Return a simple background value (estimate/truth) given the pre-specified model

 get the upper and lower average limits based on the specified model.
 No uncertainties are considered for the Poisson weights in the averaging sum

 get the upper and lower limits for the outcome corresponding to
   a given quantile.
   For integral=0.5 this gives the median limits
   in repeated experiments. The returned out_x is the corresponding
   (e.g. median) value of x.
   No uncertainties are considered for the Poisson weights when calculating
the Poisson integral 

 get the upper and lower limits for the most likely outcome.
   The returned out_x is the corresponding value of x
No uncertainties are considered for the Poisson weights when finding ML 

 get the value of x corresponding to rejection of the null hypothesis.
 This means a lower limit >0 with the pre-specified Confidence Level.
 Optionally give maxtry; the maximum value of x to try. Of not, or if
 maxtry<0 an automatic mode is used.

Deprecated name for SetBounding.

Dump internals. Print members.

 Deprecated and error prone model selection interface.
   It's use is trongly discouraged. 'mid' is the model ID (1 to 7).
This method is provided for backwards compatibility/developer use only. 
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    z   : number of MC events observed
    bm  : estimate of the background
    em  : estimate of the efficiency
    e   : true efficiency (considered known)
    mid : internal model id (really, you should not use this method at all)
    sde : efficiency estimate's standard deviation
    sdb : background estimate's standard deviation
    tau : ratio parameter (read TRolke.cxx for details)
    b   : background expectation value (considered known)
    m   : number of MC events generated

    x   : number of observed events in the experiment
    y   : number of observed events in background region
    z   : number of MC events observed
    bm  : estimate of the background
    em  : estimate of the efficiency
    e   : true efficiency (considered known)
    mid : internal model id
    sde : efficiency estimate's standard deviation
    sdb : background estimate's standard deviation
    tau : ratio parameter (read TRolke.cxx for details)
    b   : background expectation value (considered known)
    m   : number of MC events generated

Clear internal model

 ComputeInterval, the internals.
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    z   : number of MC events observed
    bm  : estimate of the background
    em  : estimate of the efficiency
    e   : true efficiency (considered known)
    mid : internal model id (really, you should not use this method at all)
    sde : efficiency estimate's standard deviation
    sdb : background estimate's standard deviation
    tau : ratio parameter (read TRolke.cxx for details)
    b   : background expectation value (considered known)
    m   : number of MC events generated

 Internal helper function 'Interval'

    x   : number of observed events in the experiment
    y   : number of observed events in background region
    z   : number of MC events observed
    bm  : estimate of the background
    em  : estimate of the efficiency
    e   : true efficiency (considered known)
    mid : internal model id (really, you should not use this method at all)
    sde : efficiency estimate's standard deviation
    sdb : background estimate's standard deviation
    tau : ratio parameter (read TRolke.cxx for details)
    b   : background expectation value (considered known)
    m   : number of MC events generated

Internal helper function
 Chooses between the different profile likelihood functions to use for the
 different models.
 Returns evaluation of the profile likelihood functions.

 Calculates the Profile Likelihood for MODEL 1:
  Poisson background/ Binomial Efficiency
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)

 Profile Likelihood function for MODEL 1:
 Poisson background/ Binomial Efficiency

 Helper for calculation of estimates of efficiency and background for model 1

gradient model likelihood

 Calculates the Profile Likelihood for MODEL 2:
  Poisson background/ Gauss Efficiency
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)

 Profile Likelihood function for MODEL 2:
 Poisson background/Gauss Efficiency

 Calculates the Profile Likelihood for MODEL 3:
 Gauss  background/ Gauss Efficiency
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)

 Profile Likelihood function for MODEL 3:
 Gauss background/Gauss Efficiency

 Calculates the Profile Likelihood for MODEL 4:
 Poiss  background/Efficiency known
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)

 Profile Likelihood function for MODEL 4:
 Poiss background/Efficiency known

 Calculates the Profile Likelihood for MODEL 5:
 Gauss  background/Efficiency known
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)

 Profile Likelihood function for MODEL 5:
 Gauss background/Efficiency known

 Calculates the Profile Likelihood for MODEL 6:
 Background known/Efficiency binomial
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)

 Profile Likelihood function for MODEL 6:
 background known/ Efficiency binomial

 Calculates the Profile Likelihood for MODEL 7:
 background known/Efficiency Gauss
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)

 Profile Likelihood function for MODEL 6:
 background known/ Efficiency gaussian

 evaluate polynomial

 evaluate mononomial

 LogFactorial function (use the logGamma function via the relation Gamma(n+1) = n!

Constructor

Get and set the Confidence Level

Set the Confidence Level in terms of Sigmas.

 Get the bounding mode flag. True activates bounded mode. Read
TRolke.cxx and the references therein for details. 

 Get the bounding mode flag. True activates bounded mode. Read
TRolke.cxx and the references therein for details.