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The following functions have been replaced by generics for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PPP_Model_Exp_Layer_Loss has been replaced by Layer_Mean
PPP_Model_Layer_Var has been replaced by Layer_Var
PPP_Model_Layer_Sd has been replaced by Layer_Sd
PPP_Model_Excess_Frequency has been replaced by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PPP_Model_Simulate has been replaced by Simulate_Losses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