Package: systemicrisk 0.4.3
systemicrisk: Systemic Risk and Network Reconstruction
Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction, where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) <doi:10.1287/mnsc.2016.2546>. It also incorporates models that use a power law distribution on the degree distribution.
Authors:
systemicrisk_0.4.3.tar.gz
systemicrisk_0.4.3.zip(r-4.5)systemicrisk_0.4.3.zip(r-4.4)systemicrisk_0.4.3.zip(r-4.3)
systemicrisk_0.4.3.tgz(r-4.4-x86_64)systemicrisk_0.4.3.tgz(r-4.4-arm64)systemicrisk_0.4.3.tgz(r-4.3-x86_64)systemicrisk_0.4.3.tgz(r-4.3-arm64)
systemicrisk_0.4.3.tar.gz(r-4.5-noble)systemicrisk_0.4.3.tar.gz(r-4.4-noble)
systemicrisk_0.4.3.tgz(r-4.4-emscripten)systemicrisk_0.4.3.tgz(r-4.3-emscripten)
systemicrisk.pdf |systemicrisk.html✨
systemicrisk/json (API)
NEWS
# Install 'systemicrisk' in R: |
install.packages('systemicrisk', repos = c('https://agandy.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 months agofrom:cb44589c25. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 02 2024 |
R-4.5-win-x86_64 | OK | Nov 02 2024 |
R-4.5-linux-x86_64 | OK | Nov 02 2024 |
R-4.4-win-x86_64 | OK | Nov 02 2024 |
R-4.4-mac-x86_64 | OK | Nov 02 2024 |
R-4.4-mac-aarch64 | OK | Nov 02 2024 |
R-4.3-win-x86_64 | OK | Nov 02 2024 |
R-4.3-mac-x86_64 | OK | Nov 02 2024 |
R-4.3-mac-aarch64 | OK | Nov 02 2024 |
Exports:calibrate_ERcalibrate_ER.nonsquarecalibrate_FitnessEmpchoosethincloneMatrixdefaultdefault_cascadedefault_clearingdiagnoseERE_step_cyclefindFeasibleMatrixfindFeasibleMatrix_targetmeangenLgetfeasibleMatrGibbsSteps_kcycleModel.additivelink.exponential.fitnessModel.fitness.conditionalmeandegreeModel.fitness.genlambdaparpriorModel.fitness.meandegreeModel.Indep.p.lambdaModel.lambda.constantModel.lambda.constant.nonsquareModel.lambda.GammaPriorModel.lambda.Gammaprior_multModel.p.BetaPriorModel.p.Betaprior_multModel.p.constantModel.p.constant.nonsquareModel.p.Fitness.Servediosample_EREsample_HierarchicalModelsteps_ERE
Example: Hierarchical Models
Rendered fromHierarchicalModels.Rmd
usingknitr::knitr
on Nov 02 2024.Last update: 2024-05-06
Started: 2015-12-21
Non-square Matrices
Rendered fromNonSquare.Rmd
usingknitr::knitr
on Nov 02 2024.Last update: 2019-01-13
Started: 2017-11-14
Some Introductory Examples
Rendered fromIntroduction.Rmd
usingknitr::knitr
on Nov 02 2024.Last update: 2024-05-06
Started: 2015-03-20