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• Pareto 2.4.5

• Minor bug fix in automated tests

Pareto 2.4.3

• Minor bug corrected in PiecewisePareto_ML_Estimator_Alpha

Pareto 2.4.2

• Added functionality for Pareto and GenPareto in Fit_References

Pareto 2.4.0

• 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

Pareto 2.3.0

• 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

Pareto 2.2.2

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

Pareto 2.2.1

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

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• The following functions have been replaced by generics for PPP_Models and PGP_Models:
  • PPP_Model_Exp_Layer_Loss has been replaced by Layer_Mean
  • PPP_Model_Layer_Var has been replaced by Layer_Var
  • PPP_Model_Layer_Sd has been replaced by Layer_Sd
  • PPP_Model_Excess_Frequency has been replaced by Excess_Frequency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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.