Changes in version 2.4.3                        

  - Minor bug corrected in PiecewisePareto_ML_Estimator_Alpha

                 Changes in version 2.4.2 (2021-03-03)                  

  - Added functionality for Pareto and GenPareto in Fit_References

                 Changes in version 2.4.0 (2021-02-18)                  

  - Improved functionality for maximum likelihood estimation
      - Possibility to use reporting thresholds
      - Allow to consider censored data
      - Improved performance
  - Added distributions in function Local_Pareto_Alpha:
      - Pareto distribution
      - Generalized Pareto distribution
      - Piecewise Pareto distribution
  - Improved handling of inputs of length zero in vectorized functions

                 Changes in version 2.3.0 (2021-02-07)                  

  - Vectorization of the following functions:
      - Pareto_Layer_Mean
      - Pareto_Layer_Var
      - Pareto_Layer_SM
      - Pareto_Extrapolation
      - Pareto_Find_Alpha_btw_Layers
      - Pareto_Find_Alpha_btw_FQ_Layer
      - Pareto_Find_Alpha_btw_FQs
      - PiecewisePareto_Layer_Mean (only parameters Cover and
        AttachmentPoint)
      - PiecewisePareto_Layer_SM (only parameters Cover and
        AttachmentPoint)
      - PiecewisePareto_Layer_Var (only parameters Cover and
        AttachmentPoint)
      - pPareto
      - dPareto
      - qPareto
      - pGenPareto
      - dGenPareto
      - qGenPareto
      - GenPareto_Layer_Mean
      - GenPareto_Layer_Var
      - GenPareto_Layer_SM

                 Changes in version 2.2.2 (2021-01-27)                  

  - Added function Fit_PML_Curve which fits a PPP_Model to a PML curve..

                 Changes in version 2.2.1 (2020-09-11)                  

  - Added the option to use weights in Pareto_ML_Estimator_Alpha,
    PiecewisePareto_ML_Estimator_Alpha and GenPareto_ML_Estimator_Alpha.

                 Changes in version 2.2.0 (2020-08-03)                  

  - Added function Fit_References for the piecewise Pareto distribution.
    This function fits a PPP model to the expected losses of given
    reference layers and excess frequencies
  - It is now possible to have layers with an expected loss of zero in
    PiecewisePareto_Match_Layer_Losses
  - Improved handling of Frequencies and TotalLoss_Frequencies in
    PiecewisePareto_Match_Layer_Losses

                 Changes in version 2.1.0 (2020-07-09)                  

  - Added functions for the generalized Pareto distribution
  - Added the class PGP_Model. PGP stands for Panjer & Generalized
    Pareto. A PGP_Model object contains the information to specify a
    collective model with a Panjer distributed claim count and a
    generalized Pareto distributed severity
  - The following functions have been replaced by generics for
    PPP_Models and PGP_Models:
      - PPP_Model_Exp_Layer_Loss has been replaced by Layer_Mean
      - PPP_Model_Layer_Var has been replaced by Layer_Var
      - PPP_Model_Layer_Sd has been replaced by Layer_Sd
      - PPP_Model_Excess_Frequency has been replaced by Excess_Frequency
      - PPP_Model_Simulate has been replaced by Simulate_Losses

                 Changes in version 2.0.0 (2020-05-02)                  

  - PiecewisePareto_Match_Layer_Losses now returns a PPP_Model object.
    PPP stands for Panjer & Piecewise Pareto. The Panjer class contains
    the Poisson, the Negative Binomial and the Binomial distribution. A
    PPP_Model object contains the information required to specify a
    collective model with a Panjer distributed claim count and a
    Piecewise Pareto distributed severity.
  - The package provides additional functions for PPP_Model objects:
      - PPP_Model_Exp_Layer_Loss: Calculates the expected loss of a
        reinsurance layer for a PPP_Model
      - PPP_Model_Layer_Var: Calculates the variance of the loss in a
        reinsurance layer for a PPP_Model
      - PPP_Model_Layer_Sd: Calculates the standard deviation of the
        loss in a reinsurance layer for a PPP_Model
      - PPP_Model_Excess_Frequency: Calculates the expected frequency in
        excess of a threshold for a PPP_Model
      - PPP_Model_Simulate: Simulates losses of a PPP_Model

                 Changes in version 1.1.5 (2020-04-03)                  

  - PiecewisePareto_Match_Layer_Losses now also works for only one layer
  - Improved error handling in PiecewisePareto_Match_Layer_Losses

                 Changes in version 1.1.3 (2020-02-13)                  

  - Added maximum likelihood estimation of the alphas of a piecewise
    Pareto distribution.
  - Allow for a different reporting threshold for each loss in
    Pareto_ML_Estimator_Alpha and in rPareto.
  - Improved fitting algorithm in Pareto_ML_Estimator_Alpha.
  - Better error handling in in Pareto_Find_Alpha_btw_FQ_Layer.

                 Changes in version 1.1.0 (2019-12-17)                  

Stable version.