class AliMathBase: public TObject

 Default constructor

 Destructor

 Robust estimator in 1D case MI version - (faster than ROOT version)

 For the univariate case
 estimates of location and scatter are returned in mean and sigma parameters
 the algorithm works on the same principle as in multivariate case -
 it finds a subset of size hh with smallest sigma, and then returns mean and
 sigma of this subset

 Modified version of ROOT robust EvaluateUni
 robust estimator in 1D case MI version
 added external factor to include precision of external measurement

  Sort eleements according occurancy
  The size of output array has is 2*n

 LTM

  Fit histogram with gaussian function

  Prameters:
       return value- chi2 - if negative ( not enough points)
       his        -  input histogram
       param      -  vector with parameters
       xmin, xmax -  range to fit - if xmin=xmax=0 - the full histogram range used
  Fitting:
  1. Step - make logarithm
  2. Linear  fit (parabola) - more robust - always converge
  3. In case of small statistic bins are averaged

  Fit histogram with gaussian function

  Prameters:
     nbins: size of the array and number of histogram bins
     xMin, xMax: histogram range
     param: paramters of the fit (0-Constant, 1-Mean, 2-Sigma, 3-Sum)
     matrix: covariance matrix -- not implemented yet, pass dummy matrix!!!

  Return values:
    >0: the chi2 returned by TLinearFitter
    -3: only three points have been used for the calculation - no fitter was used
    -2: only two points have been used for the calculation - center of gravity was uesed for calculation
    -1: only one point has been used for the calculation - center of gravity was uesed for calculation
    -4: invalid result!!

  Fitting:
  1. Step - make logarithm
  2. Linear  fit (parabola) - more robust - always converge

  calculate center of gravity rms and sum for array 'arr' with nBins an a x range xMin to xMax
  return COG; in case of failure return xMin

 Fast implementation of the complementary error function
 The error of the approximation is |eps(x)| < 5E-4
 See Abramowitz and Stegun, p.299, 7.1.27

 Test performance of the parabolic - gaussian fit - compare it with
 ROOT gauss fit
  nhistos - number of histograms to be used for test

 delta - number of bins to integrate
 type - 0 - mean value

 delta - number of bins to integrate
 type - 0 - mean value

 return number generated according to a gaussian distribution N(mean,sigma) truncated at cutat

 return number generated according to a gaussian distribution N(mean,sigma)
 truncated at leftCut and rightCut

 This is the empirical ALEPH parameterization of the Bethe-Bloch formula.
 It is normalized to 1 at the minimum.

 bg - beta*gamma

 The default values for the kp* parameters are for ALICE TPC.
 The returned value is in MIP units

 from
 Hisashi Tanizaki
 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.158.3866&rep=rep1&type=pdf
 A. Morsch 14/01/2014