- 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

- 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- The following functions have been replaced by generics for PPP_Models and PGP_Models:
- PPP_Model_Exp_Layer_Loss has been replaced by Layer_Mean
- PPP_Model_Layer_Var has been replaced by Layer_Var
- PPP_Model_Layer_Sd has been replaced by Layer_Sd
- PPP_Model_Excess_Frequency has been replaced by Excess_Frequency
- PPP_Model_Simulate has been replaced by Simulate_Losses

- 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

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

- 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.

Stable version.