Pareto 2.2.0

• 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

Pareto 2.1.0

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• The following functions have been replaced by generics for PPP_Models and PGP_Models:
  • PPP_Model_Exp_Layer_Loss has been replaced by Layer_Mean
  • PPP_Model_Layer_Var has been replaced by Layer_Var
  • PPP_Model_Layer_Sd has been replaced by Layer_Sd
  • PPP_Model_Excess_Frequency has been replaced by Excess_Frequency
  • PPP_Model_Simulate has been replaced by Simulate_Losses

Pareto 2.0.0

• 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

Pareto 1.1.5

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

Pareto 1.1.3

• 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.

Pareto 1.1.0

Stable version.