Package: Pareto 2.4.5
Pareto: The Pareto, Piecewise Pareto and Generalized Pareto Distribution
Utilities for the Pareto, piecewise Pareto and generalized Pareto distribution that are useful for reinsurance pricing. In particular, the package provides a non-trivial algorithm that can be used to match the expected losses of a tower of reinsurance layers with a layer-independent collective risk model. The theoretical background of the matching algorithm and most other methods are described in Ulrich Riegel (2018) <doi:10.1007/s13385-018-0177-3>.
Authors:
Pareto_2.4.5.tar.gz
Pareto_2.4.5.zip(r-4.6)Pareto_2.4.5.zip(r-4.5)
Pareto_2.4.5.tgz(r-4.6-any)Pareto_2.4.5.tgz(r-4.5-any)
Pareto_2.4.5.tar.gz(r-4.6-any)Pareto_2.4.5.tar.gz(r-4.5-any)
Pareto_2.4.5.tgz(r-4.5-emscripten)
Pareto.pdf |Pareto.html✨
Pareto/json (API)
NEWS
| # Install 'Pareto' in R: |
| install.packages('Pareto', repos = c('https://ulrichriegel.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ulrichriegel/pareto/issues
- Example1_AP - Example data: Attachment Points
- Example1_EL - Example data: Expected Losses
Last updated from:4da101e028. Checks:8 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 129 | ||
| source / vignettes | OK | 164 | ||
| linux-release-x86_64 | OK | 135 | ||
| macos-devel-arm64 | OK | 150 | ||
| macos-release-arm64 | OK | 138 | ||
| windows-devel | OK | 96 | ||
| windows-release | OK | 153 | ||
| wasm-release | OK | 97 |
Exports:dGenParetodParetodPiecewiseParetoExcess_FrequencyFit_PML_CurveFit_ReferencesGenPareto_Layer_MeanGenPareto_Layer_SMGenPareto_Layer_VarGenPareto_ML_Estimator_Alphais.PGP_Modelis.PPP_Modelis.valid.PGP_Modelis.valid.PPP_ModelLayer_MeanLayer_SdLayer_VarLocal_Pareto_AlphaPareto_CDFPareto_ExtrapolationPareto_Find_Alpha_btw_FQ_LayerPareto_Find_Alpha_btw_FQsPareto_Find_Alpha_btw_LayersPareto_Layer_MeanPareto_Layer_SMPareto_Layer_VarPareto_ML_Estimator_AlphaPareto_PDFpGenParetoPGP_ModelPiecewisePareto_CDFPiecewisePareto_Layer_MeanPiecewisePareto_Layer_SMPiecewisePareto_Layer_VarPiecewisePareto_Match_Layer_LossesPiecewisePareto_ML_Estimator_AlphaPiecewisePareto_PDFpParetopPiecewiseParetoPPP_ModelPPP_Model_Excess_FrequencyPPP_Model_Exp_Layer_LossPPP_Model_Layer_SdPPP_Model_Layer_VarPPP_Model_SimulateqGenParetoqParetoqPiecewiseParetorGenParetorParetorPiecewiseParetoSimulate_Losses
Dependencies:
