Pareto 2.4.5

Minor bug fix in automated tests

- Minor bug corrected in PiecewisePareto_ML_Estimator_Alpha

- Added functionality for Pareto and GenPareto in Fit_References

- 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

- 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

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

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

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- The following functions have been replaced by generics for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- PPP_Model_Exp_Layer_Loss has been replaced by Layer_Mean
- PPP_Model_Layer_Var has been replaced by Layer_Var
- PPP_Model_Layer_Sd has been replaced by Layer_Sd
- PPP_Model_Excess_Frequency has been replaced by Excess_Frequency
- PPP_Model_Simulate has been replaced by Simulate_Losses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