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:Axel Gandy [aut, cre], Luitgard A.M. Veraart [aut]

systemicrisk_0.4.3.tar.gz
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systemicrisk_0.4.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
systemicrisk/json (API)

# Install 'systemicrisk' in R:
install.packages('systemicrisk', repos = c('https://agandy.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

3.98 score 5 stars 64 scripts 390 downloads 32 exports 2 dependencies

Last updated from:cb44589c25. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK135
linux-devel-x86_64OK108
source / vignettesOK223
linux-release-arm64OK87
linux-release-x86_64OK107
macos-release-arm64OK148
macos-release-x86_64OK361
macos-oldrel-arm64OK129
macos-oldrel-x86_64OK394
windows-devel-arm64OK338
windows-devel-x86_64OK131
windows-release-arm64OK351
windows-release-x86_64OK124
windows-oldrel-x86_64OK98
wasm-releaseOK101

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

Dependencies:lpSolveRcpp

Example: Hierarchical Models
An example using the fitness model | Diagnostic of the R-output
Rendered fromHierarchicalModels.Rmdusingknitr::knitr

Last update: 2024-05-06
Started: 2015-12-21

Some Introductory Examples
An example with 3 banks | Diagnostic of the R-output | Default of banks
Rendered fromIntroduction.Rmdusingknitr::knitr

Last update: 2024-05-06
Started: 2015-03-20

Non-square Matrices
Rendered fromNonSquare.Rmdusingknitr::knitr

Last update: 2019-01-13
Started: 2017-11-14

Readme and manuals

Help Manual

Help pageTopics
Calibrate ER model to a given densitycalibrate_ER
Calibrate ER model to a given density with a nonsquare matrixcalibrate_ER.nonsquare
Calibrate empirical fitness model to a given densitycalibrate_FitnessEmp
Calibrate Thinningchoosethin
Creates a deep copy of a matrixcloneMatrix
Default of Banksdefault
Default Cascadedefault_cascade
Clearing Vector with Bankruptcy Costsdefault_clearing
Outputs Effective Sample Size Diagonistics for MCMC rundiagnose
Does one Gibbs Step on a cycleERE_step_cycle
Finds a Nonnegative Matrix Satisfying Row and Column SumsfindFeasibleMatrix
Creates a feasible starting matrix with a desired mean average degreefindFeasibleMatrix_targetmean
Generate Liabilities Matrix from PriorgenL
Creates a feasible starting matrixgetfeasibleMatr
Gibbs sampling step of a matrix in the ERE modelGibbsSteps_kcycle
Fitness model for liabilities matrixModel.additivelink.exponential.fitness
Mean out-degree of a node with given fitness in the fitness modelModel.fitness.conditionalmeandegree
Prior distribution for eta and zeta in the fitness modelModel.fitness.genlambdaparprior
Mean out-degree of a random node the fitness modelModel.fitness.meandegree
Combination of Independent Models for p and lambdaModel.Indep.p.lambda
Model for a Constant lambdaModel.lambda.constant
Model for a Constant lambda and Non-Square MatricesModel.lambda.constant.nonsquare
Model with Gamma Prior on LambdaModel.lambda.GammaPrior
Model Using Multiple Independent ComponentsModel.lambda.Gammaprior_mult
Model for a Random One-dimensional pModel.p.BetaPrior
Model Using Multiple Independent ComponentsModel.p.Betaprior_mult
Model for a Constant pModel.p.constant
Model for a constant p and Non-Square MatricesModel.p.constant.nonsquare
Multiplicative Fitness Model for Power LawModel.p.Fitness.Servedio
Sample from the ERE model with given row and column sumssample_ERE
Sample from Hierarchical Model with given Row and Column Sumssample_HierarchicalModel
Perform Steps of the Gibbs Sampler of the ERE modelsteps_ERE