systemicrisk: A Toolbox for Systemic Risk

A toolbox for systemic risk based on liabilities matrices. Contains a Gibbs sampler for liabilities matrices where only row and column sums of the liabilities matrix as well as some other fixed entries are observed. Includes models for power law distribution on the degree distribution.

Install the latest version of this package by entering the following in R:
install.packages("systemicrisk")
AuthorAxel Gandy and Luitgard A.M. Veraart
Date of publication2017-01-08 13:37:33
MaintainerAxel Gandy <a.gandy@imperial.ac.uk>
LicenseGPL-3
Version0.4

View on CRAN

Man pages

calibrate_ER: Calibrate ER model to a given density

calibrate_FitnessEmp: Calibrate empirical fitness model to a given density

choosethin: Calibrate Thinning

cloneMatrix: Crates a deep copy of a matrix

default: Default of Banks

default_cascade: Default Cascade

default_clearing: Clearing Vector with Bankruptcy Costs

diagnose: Outputs Effective Sample Size Diagonis for MCMC run

ERE_step_cycle: Does one Gibbs Step on a cycle

findFeasibleMatrix: Finds a Nonnegative Matrix Satisfying Row and Column Sums

findFeasibleMatrix_targetmean: Creates a feasible starting matrix with a desired mean...

genL: Generate Liabilities Matrix from Prior

getfeasibleMatr: Creates a feasible starting matrix

GibbsSteps_kcycle: Gibbs sampling step of a matrix in the ERE model

Model.additivelink.exponential.fitness: Fitness model for liabilities matrix

Model.fitness.conditionalmeandegree: Mean out-degree of a node with given fitness in the fitness...

Model.fitness.genlambdaparprior: Prior distribution for eta and zeta in the fitness model

Model.fitness.meandegree: Mean out-degree of a random node the fitness model

Model.Indep.p.lambda: Combination of Independent Models for p and lambda

Model.lambda.constant: Model for a Constant lambda

Model.lambda.GammaPrior: Model with Gamma Prior on Lambda

Model.lambda.Gammaprior_mult: Model Using Multiple Independent Components

Model.p.BetaPrior: Model for a Random One-dimensional p

Model.p.Betaprior_mult: Model Using Multiple Independent Components

Model.p.constant: Model for a Constant p

Model.p.Fitness.Servedio: Multiplicative Fitness Model for Power Law

sample_ERE: Sample from the ERE model with given row and column sums

sample_HierarchicalModel: Sample from Hierarchical Model with given Row and Column Sums

steps_ERE: Perform Steps of the Gibbs Sampler of the ERE model

Functions

calibrate_ER Man page
calibrate_FitnessEmp Man page
choosethin Man page
cloneMatrix Man page
default Man page
default_cascade Man page
default_clearing Man page
diagnose Man page
ERE_step_cycle Man page
findFeasibleMatrix Man page
findFeasibleMatrix_targetmean Man page
genL Man page
getfeasibleMatr Man page
GibbsSteps_kcycle Man page
Model.additivelink.exponential.fitness Man page
Model.fitness.conditionalmeandegree Man page
Model.fitness.genlambdaparprior Man page
Model.fitness.meandegree Man page
Model.Indep.p.lambda Man page
Model.lambda.constant Man page
Model.lambda.GammaPrior Man page
Model.lambda.Gammaprior_mult Man page
Model.p.BetaPrior Man page
Model.p.Betaprior_mult Man page
Model.p.constant Man page
Model.p.Fitness.Servedio Man page
sample_ERE Man page
sample_HierarchicalModel Man page
steps_ERE Man page

Files

inst
inst/CITATION
inst/NEWS.Rd
inst/doc
inst/doc/Introduction.Rmd
inst/doc/Introduction.R inst/doc/HierarchicalModels.R
inst/doc/HierarchicalModels.Rmd
inst/doc/Introduction.html
inst/doc/HierarchicalModels.html
tests
tests/testthat.R
tests/testthat
tests/testthat/test_HierarchicalModels_choosethin.R tests/testthat/test_HierarchicalModels.R tests/testthat/test-gibbs_kcycle.R tests/testthat/test_FeasibleMatrix.R
src
src/lib_FeasibleMatrix.cpp
src/lib_sample_kcycle.cpp
src/RcppExports.cpp
NAMESPACE
R
R/ExponentialFitnessLinkFun.R R/lib_Network_Evaluations.R R/lib_FindFeasibleMatrix.R R/AdjustableModels.R R/HierarchicalModels.R R/lib_ErdosRenyExponential_MCMC.R R/RcppExports.R
vignettes
vignettes/Introduction.Rmd
vignettes/HierarchicalModels.Rmd
MD5
build
build/vignette.rds
DESCRIPTION
man
man/sample_HierarchicalModel.Rd man/calibrate_FitnessEmp.Rd man/default_clearing.Rd man/Model.fitness.meandegree.Rd man/findFeasibleMatrix_targetmean.Rd man/Model.p.Fitness.Servedio.Rd man/diagnose.Rd man/Model.lambda.Gammaprior_mult.Rd man/default_cascade.Rd man/sample_ERE.Rd man/Model.p.Betaprior_mult.Rd man/Model.lambda.GammaPrior.Rd man/Model.p.BetaPrior.Rd man/findFeasibleMatrix.Rd man/default.Rd man/choosethin.Rd man/Model.additivelink.exponential.fitness.Rd man/Model.fitness.genlambdaparprior.Rd man/steps_ERE.Rd man/genL.Rd man/Model.Indep.p.lambda.Rd man/cloneMatrix.Rd man/Model.lambda.constant.Rd man/ERE_step_cycle.Rd man/calibrate_ER.Rd man/Model.p.constant.Rd man/GibbsSteps_kcycle.Rd man/getfeasibleMatr.Rd man/Model.fitness.conditionalmeandegree.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.